The muHVT package is a collection of R functions to facilitate building topology preserving maps for rich multivariate data. Tending towards a big data preponderance, a large number of rows. A collection of R functions for this typical workflow is organized below :
Data Compression: Vector quantization (VQ), HVQ (hierarchical vector quantization) using means or medians. This step compresses the rows (long data frame) using a compression objective
Data Projection: Dimension projection of the compressed cells to 1D,2D and 3D with the Sammons Non-linear Algorithm. This step creates topology preserving map (also called as embedding) coordinates into the desired output dimension .
Tessellation: Create cells required for object visualization using the Voronoi Tessellation method, package includes heatmap plots for hierarchical Voronoi tessellations (HVT). This step enables data insights, visualization, and interaction with the topology preserving map. Useful for semi-supervised tasks
Prediction: Scoring new data sets and recording their assignment using the map objects from the above steps, in a sequence of maps if required
This package can perform vector quantization using the following algorithms -
The second and third steps are iterated until a predefined number of iterations is reached or the clusters converge. The runtime for the algorithm is O(n).
The second and third steps are iterated until a predefined number of iterations is reached or the clusters converge. The runtime for the algorithm is O(k * (n-k)^2) .
These algorithm divides the dataset recursively into cells using \(k-means\) or \(k-medoids\) algorithm. The maximum number of subsets are decided by setting \(n_cells\) to, say five, in order to divide the dataset into maximum of five subsets. These five subsets are further divided into five subsets(or less), resulting in a total of twenty five (5*5) subsets. The recursion terminates when the cells either contain less than three data point or a stop criterion is reached. In this case, the stop criterion is set to when the cell error exceeds the quantization threshold.
The steps for this method are as follows :
The stop criterion is when the quantization error of a cell satisfies one of the below conditions
The quantization error for a cell is defined as follows :
\[QE = \max_i(||A-F_i||_{p})\]
where
Let us try to understand quantization error with an example.
Figure 1: The Voronoi tessellation for level 1 shown for the 5 cells with the points overlayed
An example of a 2 dimensional VQ is shown above.
In the above image, we can see 5 cells with each cell containing a certain number of points. The centroid for each cell is shown in blue. These centroids are also known as codewords since they represent all the points in that cell. The set of all codewords is called a codebook.
Now we want to calculate quantization error for each cell. For the
sake of simplicity, let’s consider only one cell having centroid
A and m data points \(F_i\) for calculating quantization
error.
For each point, we calculate the distance between the point and the centroid.
\[ d = ||A - F_i||_{p} \]
In the above equation, p = 1 means L1_Norm distance
whereas p = 2 means L2_Norm distance. In the package, the
L1_Norm distance is chosen by default. The user can pass
either L1_Norm, L2_Norm or a custom function
to calculate the distance between two points in n dimensions.
\[QE = \max_i(||A-F_i||_{p})\]
Now, we take the maximum calculated distance of all m points. This
gives us the furthest distance of a point in the cell from the centroid,
which we refer to as Quantization Error. If the
Quantization Error is higher than the given threshold, the centroid/
codevector is not a good representation for the points in the cell. Now
we can perform further Vector Quantization on these points and repeat
the above steps.
Please note that the user can select mean, max or any custom function
to calculate the Quantization Error. The custom function takes a vector
of m value (where each value is a distance between point in
n dimensions and centroids) and returns a single value
which is the Quantization Error for the cell.
If we select mean as the error metric, the above
Quantization Error equation will look like this :
\[QE = \frac{1}{m}\sum_{i=1}^m||A-F_i||_{p}\]
Sammon’s projection is an algorithm that maps a high-dimensional space to a space of lower dimensionality while attempting to preserve the structure of inter-point distances in the projection. It is particularly suited for use in exploratory data analysis and is usually considered a non-linear approach since the mapping cannot be represented as a linear combination of the original variables. The centroids are plotted in 2D after performing Sammon’s projection at every level of the tessellation.
Denoting the distance between \(i^{th}\) and \(j^{th}\) objects in the original space by \(d_{ij}^*\), and the distance between their projections by \(d_{ij}\). Sammon’s mapping aims to minimize the below error function, which is often referred to as Sammon’s stress or Sammon’s error
\[E=\frac{1}{\sum_{i<j} d_{ij}^*}\sum_{i<j}\frac{(d_{ij}^*-d_{ij})^2}{d_{ij}^*}\]
The minimization of this can be performed either by gradient descent, as proposed initially, or by other means, usually involving iterative methods. The number of iterations need to be experimentally determined and convergent solutions are not always guaranteed. Many implementations prefer to use the first Principal Components as a starting configuration.
A Voronoi diagram is a way of dividing space into a number of regions. A set of points (called seeds, sites, or generators) is specified beforehand and for each seed, there will be a corresponding region consisting of all points within proximity of that seed. These regions are called Voronoi cells. It is complementary to Delaunay triangulation.
Tessellate: Constructing Voronoi Tesselations
In this package, we use sammons from the package
MASS to project higher dimensional data to a 2D space. The
function hvq called from the HVT function
returns hierarchical quantized data which will be the input for
construction of the tessellations. The data is then represented in 2D
coordinates and the tessellations are plotted using these coordinates as
centroids. We use the package deldir for this purpose. The
deldir package computes the Delaunay triangulation (and
hence the Dirichlet or Voronoi tessellation) of a planar point set
according to the second (iterative) algorithm of Lee and Schacter. For
subsequent levels, transformation is performed on the 2D coordinates to
get all the points within its parent tile. Tessellations are plotted
using these transformed points as centroids. The lines in the
tessellations are chopped in places so that they do not protrude outside
the parent polygon. This is done for all the subsequent levels.
In this package, we use predictLayerHVT function to
predict based on the the sets of maps ( map A, map B, map C) constructed
using HVT function. For each test records, the function will assign that
record to either of Layer1 or Layer2.
Prediction Algorithm
The prediction algorithm recursively calculates the distance between each point in the test dataset and the cell centroids for each level. The following steps explain the prediction method for a single point in the test dataset :
In this section, we will see how we can use the package to visualize multidimensional data by projecting them to two dimensions using Sammon’s projection
Data Understanding
First of all, let us see how to generate data for torus. We are using
a library geozoo for this purpose. Geo Zoo (stands for
Geometric Zoo) is a compilation of geometric objects ranging from three
to 10 dimensions. Geo Zoo contains regular or well-known objects, eg
cube and sphere, and some abstract objects, e.g. Boy’s surface, Torus
and Hyper-Torus.
Here, we will generate a 3D torus (a torus is a surface of revolution generated by revolving a circle in three-dimensional space one full revolution about an axis that is coplanar with the circle) with 9000 points.
set.seed(240)
# Here p represents dimension of object
# n represents number of points
torus <- geozoo::torus(p = 3,n = 9000)
torus_df <- data.frame(torus$points)
torus_df1 <- torus_df %>% round(4)
colnames(torus_df) <- c("x","y","z")Now let’s do some EDA on the data. First of all, we will see how the data looks like. For the shake of brevity we are displaying first six rows.
Table(head(torus_df))| x | y | z |
|---|---|---|
| -2.628238 | 0.5655770 | -0.7253285 |
| -1.417917 | -0.8902793 | 0.9454533 |
| -1.030820 | 1.1066495 | -0.8730506 |
| 1.884711 | 0.1894905 | 0.9943888 |
| -1.950608 | -2.2506838 | 0.2070521 |
| -1.482371 | 0.9228529 | 0.9672467 |
Now let’s have a look at structure and summary of the data.
str(torus_df)
#> 'data.frame': 9000 obs. of 3 variables:
#> $ x: num -2.63 -1.42 -1.03 1.88 -1.95 ...
#> $ y: num 0.566 -0.89 1.107 0.189 -2.251 ...
#> $ z: num -0.725 0.945 -0.873 0.994 0.207 ...summary(torus_df)
#> x y z
#> Min. :-2.99767 Min. :-2.999343 Min. :-0.9999999
#> 1st Qu.:-1.15065 1st Qu.:-1.120632 1st Qu.:-0.7130951
#> Median :-0.01899 Median : 0.001856 Median : 0.0033675
#> Mean :-0.00914 Mean : 0.004195 Mean : 0.0001237
#> 3rd Qu.: 1.13001 3rd Qu.: 1.130708 3rd Qu.: 0.7138584
#> Max. : 2.99713 Max. : 2.999308 Max. : 1.0000000Now let’s try to visualize the torus (donut) in 3D Space.
library(rgl)
plotids <- plot3d(torus_df$x, torus_df$y, torus_df$z,
type = "s", col = c("white", "red"),
xlab = "X", ylab = "Y", zlab = "Z",
xlim = c(-5, 5), ylim = c(-5, 5), zlim = c(-5, 5))
rglwidget(elementId = "plot3drgl")Figure 2: 3D Torus
In this section all the outlined workflow steps provided in the abstract section (Compression, Projection and Tessellation) are executed at level 1.
Step 1: Data Compression
The core function for compression in the workflow is
HVQ, which is called within the HVT function.
we have a parameter called quantization error. This parameter acts as a
threshold and determines the number of levels in the hierarchy. It means
that, if there are ‘n’ number of levels in the hierarchy, then all the
clusters formed till this level will have quantization error equal or
greater than the threshold quantization error. The user can define the
number of clusters in the first level of hierarchy and then each cluster
in first level is sub-divided into the same number of clusters as there
are in the first level. This process continues and each group is divided
into smaller clusters as long as thethreshold quantization error is met.
The output of this technique will be hierarchically arranged vector
quantized data.
However, let’s try to comprehend the HVT function first before moving on.
HVT(
dataset,
min_compression_perc,
n_cells,
depth,
quant.err,
projection.scale,
normalize = T,
distance_metric = c("L1_Norm", "L2_Norm"),
error_metric = c("mean", "max"),
quant_method = c("kmeans", "kmedoids"),
diagnose = TRUE,
hvt_validation = FALSE,
train_validation_split_ratio = 0.8
)Each of the parameters of HVT function have been explained below :
dataset - A dataframe with numeric
columns
min_compression_perc - An integer
indicating the minimum percent compression rate to be achieved for the
dataset
n_cells - An integer indicating the
number of cells per hierarchy (layers)
depth - An integer indicating the
number of layers. (1 = No hierarchy, 2 = 2 layers, etc …)
quant.error - A number indicating
the quantization error threshold. A cell will only breakdown into
further cells if the quantization error of the cell is above the defined
quantization error threshold
distance_metric - The distance
metric can be L1_Norm or L2_Norm.
L1_Norm is selected by default. The distance metric is used
to calculate the distance between an n dimensional point
and centroid. The user can also pass a custom function to calculate this
distance
error_metric - The error metric can
be mean or max. max is selected
by default. max will return the max of m
values and mean will take mean of m values
where each value is a distance between a point and centroid of the cell.
Moreover, the user can also pass a custom function to calculate the
error metric
quant_method - The quantization
method can be kmeans or kmedoids.
kmeans is selected by default
normalize - A logical value
indicating whether the columns in your dataset need to be normalized.
Default value is TRUE. The algorithm supports Z-score
normalization
diagnose - A logical value
indicating whether user wants to perform diagnostics on the model.
Default value is TRUE.
hvt_validation - A logical value
indicating whether user wants to holdout a validation set and find mean
absolute deviation of the validation points from the centroid. Default
value is FALSE.
train_validation_split_ratio - A
numeric value indicating train validation split ratio. This argument is
only used when hvt_validation has been set to TRUE. Default value for
the argument is 0.8
We will use the HVT function to compress our data while
preserving essential features of the dataset. Our goal is to achieve
data compression upto atleast 80%. In situations where the
compression ratio does not meet the desired target, we can explore
adjusting the model parameters as a potential solution. This involves
making modifications to parameters such as the
quantization error threshold or
increasing the number of cells and then rerunning the HVT
function again.
In our example we will iteratively increase the number of cells until the desired compression percentage is reached instead of increasing the quantization threshold because it may reduce the level of detail captured in the data representation
We will pass the below mentioned model parameters along with torus
dataset to HVT function.
Model Parameters
set.seed(240)
hvt.torus <- muHVT::HVT(
torus_df,
n_cells = 100,
depth = 1,
quant.err = 0.1,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans"
)Let’s checkout the compression summary .
compressionSummaryTable(hvt.torus[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 100 | 0 | 0 | n_cells: 100 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
As it can be seen from the table above, none of the 100 cells have hit the quantization threshold error. Therefore we can further subdivide the cells by increasing the n_cells parameters and then see if desired compression (80%) is reached
Since, we are yet to achive atleast 80% compression. Let’s try to compress again using the below mentioned set of model parameters.
Model Parameters
set.seed(240)
hvt.torus2 <- muHVT::HVT(
torus_df,
n_cells = 300,
depth = 1,
quant.err = 0.1,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans"
)Let’s checkout the compression summary again.
compressionSummaryTable(hvt.torus2[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 300 | 43 | 0.14 | n_cells: 300 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
It can be observed from the table above that only 43 cells
out of 300 i.e. 14% of the cells hit the Quantization Error
threshold. Therefore we can further subdivide the cells by increasing
the n_cells parameters and then see if 80% compression is reached
Now let’s try again with the below mentioned set of model parameters.
Model Parameters
set.seed(240)
hvt.torus3 <- muHVT::HVT(
torus_df,
n_cells = 900,
depth = 1,
quant.err = 0.1,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans"
)Let’s check the compression summary for torus.
compressionSummaryTable(hvt.torus3[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 900 | 839 | 0.93 | n_cells: 900 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
By increasing the number of cells to 900, we were
successfully able to compress 93% of the data, so we will
not further subdivide the cells
Step 2: Data Projection
The function sammonsProjection() utilizes the
sammons function from the MASS package being called in
HVT. Sammon’s projection is an algorithm that maps a
high-dimensional space to a space of lower dimensionality while
attempting to preserve the structure of inter-point distances in the
projection.The centroids are plotted in 2D after performing Sammon’s
projection at every level of the tessellation.
lets view the projected 2D centroids after performing sammon’s projection. For the shake of brevity we are displaying first six rows.
hvt_torus_coordinates <-hvt.torus3[[2]][[1]][["1"]]
centroids <<- list()
coordinates_value <- lapply(1:length(hvt_torus_coordinates), function(x){
centroids <-hvt_torus_coordinates[[x]]
coordinates <- centroids$pt
})
centroid_coordinates<<- do.call(rbind.data.frame, coordinates_value)
colnames(centroid_coordinates) <- c("x","y")
centroid_coordinates <- centroid_coordinates %>% data.frame() %>% round(4)
Table(head(centroid_coordinates), scroll = T, limit = 20)| x | y |
|---|---|
| 20.7876 | 8.8385 |
| -15.0650 | 6.5840 |
| 4.1539 | -7.6615 |
| 9.7266 | -12.0612 |
| -10.5474 | 18.8626 |
| -6.0931 | -12.1636 |
Step 3: Tessellation
The deldir package computes the Delaunay triangulation
(and hence the Dirichlet or Voronoi tessellation) of a planar point set
according to the second (iterative) algorithm of Lee and Schacter. For
subsequent levels, transformation is performed on the 2D coordinates to
get all the points within its parent tile. Tessellations are plotted
using these transformed points as centroids.
plotHVT is the main function to plot hierarchical
voronoi tessellations.
Now let’s try to understand plotHVT function. The parameters have been explained in detail below
plotHVT(hvt.results, line.width, color.vec, pch1 = 21, centroid.size = 3, title = NULL, maxDepth = 1)hvt.results - A list containing the
output of the HVT function which has the details of the tessellations to
be plotted
line.width - A vector indicating
the line widths of the tessellation boundaries for each layer
color.vec - A vector indicating the
colors of the tessellations boundaries at each layer
pch1 - Symbol type of the centroids
of the tessellations (parent levels). Refer points (default =
21)
centroid.size - Size of centroids
of first level tessellations (default = 3)
title - Set a title for the plot
(default = NULL)
For better visualisation, let’s plot the Voronoi tessellation.
muHVT::plotHVT(
hvt.torus3,
line.width = c(0.4),
color.vec = c("#141B41"),
centroid.size = 0.6,
maxDepth = 1
)From the presented plot, the inherent structure of the donut can be easily observed in the two-dimensional space
We will now overlay all the features as heatmap over the Voronoi Tessellation plot for better visualization and identification of patterns, trends, and variations in the data. .
Let’s have look at the hvtHmap function which we will
use to overlay a variable as heatmap.
hvtHmap(hvt.results, dataset, child.level, hmap.cols, color.vec ,line.width, palette.color = 6)hvt.results - A list of results
obtained from the HVT function
dataset - A dataframe containing
the variables to overlay as a heatmap. The user can pass an external
dataset or the dataset that was used to perform hierarchical vector
quantization. The dataset should have the same number of points as the
dataset used to perform hierarchical Vector Quantization in the HVT
function
child.level - A number indicating
the level for which the heat map is to be plotted
hmap.cols - The column number of
column name from the dataset indicating the variables for which the heat
map is to be plotted. To plot the quantization error as heatmap, pass
'quant_error'. Similarly to plot the no of points in each
cell as heatmap, pass 'no_of_points' as a
parameter
color.vec - A color vector such
that length(color.vec) = child.level (default = NULL)
line.width - A line width vector
such that length(line.width) = child.level (default = NULL)
palette.color - A number indicating
the heat map color palette. 1 - rainbow, 2 - heat.colors, 3 -
terrain.colors, 4 - topo.colors, 5 - cm.colors, 6 - BlCyGrYlRd
(Blue,Cyan,Green,Yellow,Red) color (default = 6)
show.points - A boolean indicating
whether the centroids should be plotted on the tessellations (default =
FALSE)
Now let’s plot the Voronoi Tessellation with the heatmap overlaid for all the features in the torus data for better visualization and interpretation of data patterns and distributions.
metric_list <- colnames(hvt.torus3[[3]]$summary)
metric_list <- metric_list[7:9]
hmap <- list()
hmap <- lapply(1:length(metric_list), function(x){
muHVT::hvtHmap(
hvt.torus3,
torus_df,
child.level = 1,
hmap.cols = metric_list[[x]],
line.width = c(0.4),
color.vec = c("#141B41"),
palette.color = 6,
centroid.size = 1,
show.points = T,
quant.error.hmap = 0.1,
n_cells.hmap = 900
)
})The heatmaps displayed below provides a visual representation of the spatial characteristics of the torus, allowing us to observe patterns and trends in the distribution of each of the features (X,Y and Z). The sheer green shades highlight regions with higher coordinate values in each of the heatmaps, while the indigo shades indicate areas with the lowest coordinate values in each of the heatmaps. By analyzing these heatmaps, we can gain insights into the variations and relationships between each of these features within the torus structure
grid.arrange(hmap[[1]], nrow = 1, ncol=1)grid.arrange(hmap[[2]] ,nrow = 1, ncol=1)grid.arrange(hmap[[3]], nrow = 1, ncol=1)In this section we will explore a little more and try to project the torus(3D) object to 2D Space with different set of model parameters at level 2 inorder to achieve more refined and detailed tessellation
Through this exploration, we can gain a deeper understanding of the transformation of the torus object into a 2D projection. By adjusting the model parameters specifically for the second level of tessellation, we expect to achieve a higher level of refinement and granularity in the resulting projection, enabling us to analyze the torus in greater detail and extract meaningful insights from the transformed data.
Step 1: Data Compression
First we will perform Hierarchical Vector Quantization using the torus data along with below mentioned model parameters to achieve a compression summary of atleast 80%.
for detailed information on data Compression please refer to section 2 of this vignette.
Model Parameters
set.seed(240)
hvt.torus4 <- muHVT::HVT(
torus_df,
n_cells = 20,
depth = 1,
quant.err = 0.1,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans"
)Let’s checkout the compression summary for torus and see whether the model has achieved 80% compression or not.
compressionSummaryTable(hvt.torus4[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 20 | 0 | 0 | n_cells: 20 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
It can be observed from the table above that none of the cells has hit the Quantization Error threshold. Therefore we can further subdivide the cells by increasing the n_cells inorder to achieve a compression of 80%
Since we are not able to achieve our compression objective. Let’s try again with the below mentioned set of model parameters.
Model Parameters
set.seed(240)
hvt.torus5 <- muHVT::HVT(
torus_df,
n_cells = 26,
depth = 2,
quant.err = 0.1,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans"
)Now,let’s checkout the compression summary for torus by.
compressionSummaryTable(hvt.torus5[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 26 | 0 | 0 | n_cells: 26 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
| 2 | 666 | 535 | 0.8 | n_cells: 26 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
It can be observed from the table above that only 535 cells
out of 666 i.e. 80% of the cells hit the Quantization Error
threshold. Since we have attained 80% compression we will not further
subdivide the cells
Step 2: Data Projection
lets view the projected 2D centroids after performing sammon’s projection on the compressed data recieved after performing vector quantization.
For detailed information on Data Projection please refer to section 3 of this vignette.
For the shake of brevity we are displaying first six rows.
hvt_torus_coordinates <-hvt.torus5[[2]][[1]][["1"]]
centroids <<- list()
coordinates_value <- lapply(1:length(hvt_torus_coordinates), function(x){
centroids <-hvt_torus_coordinates[[x]]
coordinates <- centroids$pt
})
centroid_coordinates<<- do.call(rbind.data.frame, coordinates_value)
colnames(centroid_coordinates) <- c("x","y")
centroid_coordinates <- centroid_coordinates %>% data.frame() %>% round(4)
Table(head(centroid_coordinates), scroll = T, limit = 20
)| x | y |
|---|---|
| 20.2685 | 4.0434 |
| -12.5504 | 10.9844 |
| -2.1562 | -5.5135 |
| 4.2848 | -17.6823 |
| -17.5999 | 11.8420 |
| -13.2824 | -10.8036 |
Step 3: Tessellation
Now, we have obtained the centroid coordinates resulting from the application of Sammon’s projection.
For better visualisation, let’s plot the Voronoi tessellation using
the plotHVT function.
muHVT::plotHVT(
hvt.torus5,
line.width = c(0.6,0.4),
color.vec = c("#141B41","#0582CA"),
centroid.size = 0.8,
maxDepth = 2
)From the presented plot, the inherent structure of the donut can no longer be observed in the two-dimensional space
NOTE
When we pass the 3D torus data to the HVT function with depth 1, it performs a variance decomposition at a single level. This means that the algorithm calculates the overall variance of the data and provides a 2D visualization that represents the variance structure in a simplified manner, allowing us to visualize the data (donut structure).
However, when we increase the depth to 2, the HVT function performs a more detailed variance decomposition by breaking down the overall variance into subcomponents at two levels. This increased level of analysis introduces additional dimensions or factors that are not easily represented in a 2D visualization.
Now let’s plot the Voronoi Tessellation with the heatmap overlaid for all the features in the torus data at level 2 for better visualization.
metric_list <- colnames(hvt.torus3[[3]]$summary)
metric_list <- metric_list[7:9]
hmap <- list()
hmap <- lapply(1:length(metric_list), function(x){
muHVT::hvtHmap(
hvt.torus5,
torus_df,
child.level = 2,
hmap.cols = metric_list[[x]],
line.width = c(0.6,0.4),
color.vec = c("#141B41","#0582CA"),
palette.color = 6,
centroid.size = 1,
show.points = T,
quant.error.hmap = 0.1,
n_cells.hmap = 26
)
})The heatmaps displayed below provides a visual representation of the spatial characteristics of the torus, allowing us to observe patterns and trends in the distribution of each of the features (X,Y and Z). The sheer green shades highlight regions with higher coordinate values in each of the heatmaps, while the indigo shades indicate areas with the lowest coordinate values in each of the heatmaps. By analyzing these heatmaps, we can gain insights into the variations and relationships between each of these features within the torus structure
grid.arrange(hmap[[1]], nrow = 1, ncol=1)grid.arrange(hmap[[2]] ,nrow = 1, ncol=1)grid.arrange(hmap[[3]], nrow = 1, ncol=1)Data Understanding
In this section, we will use the
Prices of Personal Computers dataset. This dataset contains
6259 observations and 10 features. The dataset observes the price from
1993 to 1995 of 486 personal computers in the US. The variables are
price, speed, ram, screen, cd, etc. The dataset can be downloaded from
here.
In this example, we will compress this dataset by using hierarchical VQ via k-means and visualize the Voronoi Tessellation plots using Sammons projection. Later on, we will overlay all the variables as a heatmap to generate further insights.
Here, we load the data and store into a variable
computers.
set.seed(240)
# Load data from csv files
computers <- read.csv("https://raw.githubusercontent.com/Mu-Sigma/muHVT/master/vignettes/sample_dataset/Computers.csv")Let’s explore the Personal Computers Dataset. For the shake of brevity we are displaying first six rows.
# Quick peek
Table(head(computers), scroll = T, limit = 20)| X | price | speed | hd | ram | screen | cd | multi | premium | ads | trend |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1499 | 25 | 80 | 4 | 14 | no | no | yes | 94 | 1 |
| 2 | 1795 | 33 | 85 | 2 | 14 | no | no | yes | 94 | 1 |
| 3 | 1595 | 25 | 170 | 4 | 15 | no | no | yes | 94 | 1 |
| 4 | 1849 | 25 | 170 | 8 | 14 | no | no | no | 94 | 1 |
| 5 | 3295 | 33 | 340 | 16 | 14 | no | no | yes | 94 | 1 |
| 6 | 3695 | 66 | 340 | 16 | 14 | no | no | yes | 94 | 1 |
Now, let us check the structure of the data and analyse its summary.
str(computers)
#> 'data.frame': 6259 obs. of 11 variables:
#> $ X : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ price : int 1499 1795 1595 1849 3295 3695 1720 1995 2225 2575 ...
#> $ speed : int 25 33 25 25 33 66 25 50 50 50 ...
#> $ hd : int 80 85 170 170 340 340 170 85 210 210 ...
#> $ ram : int 4 2 4 8 16 16 4 2 8 4 ...
#> $ screen : int 14 14 15 14 14 14 14 14 14 15 ...
#> $ cd : chr "no" "no" "no" "no" ...
#> $ multi : chr "no" "no" "no" "no" ...
#> $ premium: chr "yes" "yes" "yes" "no" ...
#> $ ads : int 94 94 94 94 94 94 94 94 94 94 ...
#> $ trend : int 1 1 1 1 1 1 1 1 1 1 ...summary(computers)
#> X price speed hd
#> Min. : 1 Min. : 949 Min. : 25.00 Min. : 80.0
#> 1st Qu.:1566 1st Qu.:1794 1st Qu.: 33.00 1st Qu.: 214.0
#> Median :3130 Median :2144 Median : 50.00 Median : 340.0
#> Mean :3130 Mean :2220 Mean : 52.01 Mean : 416.6
#> 3rd Qu.:4694 3rd Qu.:2595 3rd Qu.: 66.00 3rd Qu.: 528.0
#> Max. :6259 Max. :5399 Max. :100.00 Max. :2100.0
#> ram screen cd multi
#> Min. : 2.000 Min. :14.00 Length:6259 Length:6259
#> 1st Qu.: 4.000 1st Qu.:14.00 Class :character Class :character
#> Median : 8.000 Median :14.00 Mode :character Mode :character
#> Mean : 8.287 Mean :14.61
#> 3rd Qu.: 8.000 3rd Qu.:15.00
#> Max. :32.000 Max. :17.00
#> premium ads trend
#> Length:6259 Min. : 39.0 Min. : 1.00
#> Class :character 1st Qu.:162.5 1st Qu.:10.00
#> Mode :character Median :246.0 Median :16.00
#> Mean :221.3 Mean :15.93
#> 3rd Qu.:275.0 3rd Qu.:21.50
#> Max. :339.0 Max. :35.00Let us first split the data into train and test. We will use 80% of the data as train and remaining as test.
noOfPoints <- dim(computers)[1]
trainLength <- as.integer(noOfPoints * 0.8)
trainComputers <- computers[1:trainLength,]
testComputers <- computers[(trainLength+1):noOfPoints,]K-means is not suitable for factor variables as the sample space for factor variables is discrete. A Euclidean distance function on such a space isn’t really meaningful. Hence, we will delete the factor variables(X, cd, multi, premium, trend) in our dataset.
Here we keep the original trainComputers and
testComputers as we will use the variables from this
dataset to overlay as heatmap and generate some insights.
trainComputers <-
trainComputers %>% dplyr::select(-c(X, cd, multi, premium, trend))
testComputers <-
testComputers %>% dplyr::select(-c(X, cd, multi, premium, trend))Now, lets have a look at the scaled training dataset containing (5007 data points). For the shake of brevity we are displaying first six rows.
trainComputers <- scale(trainComputers)
metric_list <- colnames(trainComputers)
scale_attr <- attributes(trainComputers)
trainComputers1 <- trainComputers %>% as.data.frame() %>% round(4)
Table(head(trainComputers1))| price | speed | hd | ram | screen | ads |
|---|---|---|---|---|---|
| -1.2977 | -1.1952 | -1.3134 | -0.7181 | -0.6148 | -2.3877 |
| -0.7999 | -0.7832 | -1.2896 | -1.1092 | -0.6148 | -2.3877 |
| -1.1362 | -1.1952 | -0.8853 | -0.7181 | 0.5490 | -2.3877 |
| -0.7091 | -1.1952 | -0.8853 | 0.0641 | -0.6148 | -2.3877 |
| 1.7228 | -0.7832 | -0.0766 | 1.6285 | -0.6148 | -2.3877 |
| 2.3956 | 0.9161 | -0.0766 | 1.6285 | -0.6148 | -2.3877 |
Now, lets have a look at the scaled testing dataset containing (1252 data points). For the shake of brevity we are displaying first six rows.
testComputers <- scale(testComputers, center = scale_attr$`scaled:center`, scale = scale_attr$`scaled:scale`)
testComputers1 <- testComputers %>% as.data.frame() %>% round(4)
Table(head(testComputers1))| price | speed | hd | ram | screen | ads | |
|---|---|---|---|---|---|---|
| 5008 | -1.2287 | -0.7832 | -0.6760 | -0.7181 | 0.5490 | -0.8403 |
| 5009 | 1.3848 | 0.0922 | 3.0631 | 3.1928 | 0.5490 | -0.8403 |
| 5010 | -0.8016 | 0.0922 | -0.6760 | -0.7181 | -0.6148 | -0.8403 |
| 5011 | 0.2311 | 2.6668 | -0.4096 | -0.7181 | -0.6148 | -0.8403 |
| 5012 | 0.3084 | 0.9161 | 1.7311 | 1.6285 | 0.5490 | -0.8403 |
| 5013 | -0.5072 | 0.9161 | 3.0631 | 0.0641 | -0.6148 | -0.8403 |
As we are familiar with the structure of the computers data, we will now follow the following steps to get the predictions using the Computers dataset.
Step 1: Data Compression
For more detailed information on Data Compression please refer to section 2 of this vignette.
Let us try to understand the HVT function first.
HVT(
dataset,
min_compression_perc,
n_cells,
depth,
quant.err,
projection.scale,
normalize = T,
distance_metric = c("L1_Norm", "L2_Norm"),
error_metric = c("mean", "max"),
quant_method = c("kmeans", "kmedoids"),
diagnose = TRUE,
hvt_validation = FALSE,
train_validation_split_ratio = 0.8
)Each of the parameters of HVT function have been explained below :
dataset - A dataframe with numeric
columns
min_compression_perc - An integer
indicating the minimum percent compression rate to be achieved for the
dataset
n_cells - An integer indicating the
number of cells per hierarchy (layers)
depth - An integer indicating the
number of layers. (1 = No hierarchy, 2 = 2 layers, etc …)
quant.error - A number indicating
the quantization error threshold. A cell will only breakdown into
further cells if the quantization error of the cell is above the defined
quantization error threshold
distance_metric - The distance
metric can be L1_Norm or L2_Norm.
L1_Norm is selected by default. The distance metric is used
to calculate the distance between an n dimensional point
and centroid. The user can also pass a custom function to calculate this
distance
error_metric - The error metric can
be mean or max. max is selected
by default. max will return the max of m
values and mean will take mean of m values
where each value is a distance between a point and centroid of the cell.
Moreover, the user can also pass a custom function to calculate the
error metric
quant_method - The quantization
method can be kmeans or kmedoids.
kmeans is selected by default
normalize - A logical value
indicating whether the columns in your dataset need to be normalized.
Default value is TRUE. The algorithm supports Z-score
normalization
diagnose - A logical value
indicating whether user wants to perform diagnostics on the model.
Default value is TRUE.
hvt_validation - A logical value
indicating whether user wants to holdout a validation set and find mean
absolute deviation of the validation points from the centroid. Default
value is FALSE.
train_validation_split_ratio - A
numeric value indicating train validation split ratio. This argument is
only used when hvt_validation has been set to TRUE. Default value for
the argument is 0.8
First we will perform HVT using the computers dataset along with
below mentioned model parameters to generate map A and try to achieve
data compression of atleast 80%. In situations where the
compression ratio does not meet the desired target we will iteratively
increase the number of cells until the desired compression percentage is
reached.
In this example usage we will compress the data at depth 1 as it provides a simplified overview of the data by calculating the overall variance and potentially reducing the dimensionality of the data
Model Parameters
set.seed(240)
hvt.results <- list()
map_A <- muHVT::HVT(trainComputers,
n_cells = 1001,
depth = 1,
quant.err = 0.1,
projection.scale = 10,
normalize = F,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans",
diagnose = F)Now let’s check the compression summary. The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.
compressionSummaryTable(map_A[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 1001 | 831 | 0.83 | n_cells: 1001 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
As it can be seen from the table above,
83% of the cells have hit the quantization
threshold error. Since we are successfully able to compress
83% of the data, so we will not further subdivide the cells
map_A[[3]] gives us detailed
information about the hierarchical vector quantized data.
map_A[[3]][['summary']] gives a nice
tabular data containing no of points, Quantization Error and the
codebook.
The datatable displayed below is the summary from map A
summaryTable(map_A[[3]]$summary)| Segment.Level | Segment.Parent | Segment.Child | n | Cell.ID | Quant.Error | price | speed | hd | ram | screen | ads |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 3 | 364 | 0.05 | -0.46 | 0.92 | -0.95 | -0.72 | -0.61 | 0.79 |
| 1 | 1 | 2 | 4 | 381 | 0.1 | -1.02 | 0.92 | -0.64 | -0.72 | 0.55 | -0.68 |
| 1 | 1 | 3 | 4 | 396 | 0.07 | -0.40 | 0.92 | -0.61 | -0.72 | -0.61 | -0.57 |
| 1 | 1 | 4 | 5 | 660 | 0.07 | 0.04 | 0.92 | 0.17 | 0.06 | 0.55 | 0.93 |
| 1 | 1 | 5 | 2 | 591 | 0.01 | -0.20 | 0.92 | -0.50 | 0.06 | 0.55 | 0.08 |
| 1 | 1 | 6 | 4 | 741 | 0.05 | 1.28 | -0.78 | -0.41 | -0.72 | 2.88 | -0.38 |
| 1 | 1 | 7 | 7 | 720 | 0.06 | 0.02 | 0.92 | 0.85 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 8 | 5 | 905 | 0.08 | 1.82 | 0.92 | 0.68 | 1.63 | 0.55 | 0.28 |
| 1 | 1 | 9 | 5 | 594 | 0.03 | -0.24 | 0.92 | 0.31 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 10 | 2 | 391 | 0.02 | -0.99 | 0.92 | -0.08 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 11 | 5 | 190 | 0.06 | -1.75 | -0.78 | -0.08 | -0.72 | -0.61 | -0.08 |
| 1 | 1 | 12 | 3 | 68 | 0.08 | -1.41 | -1.20 | -0.96 | -0.72 | 0.55 | 0.83 |
| 1 | 1 | 13 | 6 | 87 | 0.05 | -1.20 | -1.20 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 14 | 8 | 312 | 0.11 | -0.72 | 0.09 | -0.62 | -0.72 | -0.61 | -0.71 |
| 1 | 1 | 15 | 13 | 987 | 0.16 | 1.49 | 0.09 | 3.06 | 3.19 | 0.55 | -0.92 |
| 1 | 1 | 16 | 4 | 589 | 0.03 | 0.02 | 0.09 | -0.08 | 0.06 | 0.55 | 0.72 |
| 1 | 1 | 17 | 8 | 647 | 0.1 | 0.50 | 0.09 | -0.45 | 0.06 | 0.55 | -1.67 |
| 1 | 1 | 18 | 2 | 37 | 0.04 | -2.00 | -0.78 | -1.21 | -1.11 | -0.61 | 0.82 |
| 1 | 1 | 19 | 7 | 443 | 0.06 | 0.11 | 0.92 | -0.64 | -0.72 | -0.61 | -0.47 |
| 1 | 1 | 20 | 6 | 723 | 0.02 | 0.24 | -0.78 | 0.82 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 21 | 5 | 102 | 0.07 | 0.32 | -0.78 | -0.81 | -0.72 | -0.61 | -2.32 |
| 1 | 1 | 22 | 7 | 482 | 0.17 | -0.04 | 2.67 | -0.50 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 23 | 2 | 674 | 0.06 | 0.63 | 0.09 | 0.19 | 0.06 | 0.55 | -1.08 |
| 1 | 1 | 24 | 7 | 820 | 0.09 | 0.86 | 0.09 | 0.09 | 0.06 | 2.88 | 0.36 |
| 1 | 1 | 25 | 8 | 714 | 0.14 | 1.15 | 0.92 | -0.19 | 0.06 | 0.55 | 0.92 |
| 1 | 1 | 26 | 5 | 136 | 0.04 | -1.53 | -0.78 | -0.69 | -0.72 | -0.61 | -0.58 |
| 1 | 1 | 27 | 3 | 366 | 0.01 | -0.71 | 0.92 | -0.68 | -0.72 | -0.61 | 0.47 |
| 1 | 1 | 28 | 3 | 920 | 0.06 | 1.97 | 0.92 | -0.08 | 0.06 | 2.88 | 0.72 |
| 1 | 1 | 29 | 3 | 182 | 0.01 | -1.30 | -0.78 | -0.69 | -0.72 | -0.61 | 0.80 |
| 1 | 1 | 30 | 5 | 154 | 0.04 | -0.26 | 0.92 | -0.89 | -0.72 | -0.61 | -2.27 |
| 1 | 1 | 31 | 3 | 360 | 0.08 | -1.26 | 0.09 | -0.23 | -0.72 | 0.55 | -0.45 |
| 1 | 1 | 32 | 7 | 457 | 0.05 | -0.41 | -1.20 | 0.33 | 0.06 | -0.61 | 0.39 |
| 1 | 1 | 33 | 3 | 60 | 0.09 | -1.82 | -0.78 | -0.50 | -1.11 | 0.55 | -0.16 |
| 1 | 1 | 34 | 8 | 468 | 0.08 | -0.81 | -0.78 | 0.23 | 0.06 | 0.55 | 0.45 |
| 1 | 1 | 35 | 6 | 187 | 0.02 | -0.90 | -0.78 | -1.12 | -0.72 | -0.61 | 0.46 |
| 1 | 1 | 36 | 5 | 9 | 0.08 | -0.60 | -1.20 | -0.85 | -0.72 | 2.88 | 0.36 |
| 1 | 1 | 37 | 4 | 205 | 0.03 | -1.26 | -0.78 | -0.65 | -0.72 | 0.55 | 0.52 |
| 1 | 1 | 38 | 4 | 363 | 0.03 | -0.68 | 0.92 | -0.68 | -0.72 | -0.61 | 0.97 |
| 1 | 1 | 39 | 6 | 310 | 0.04 | -0.65 | 0.09 | -0.78 | -0.72 | -0.61 | 0.78 |
| 1 | 1 | 40 | 6 | 240 | 0.08 | -1.41 | -0.78 | -0.08 | -0.72 | 0.55 | -0.51 |
| 1 | 1 | 41 | 2 | 55 | 0.04 | -1.14 | 0.09 | -1.18 | -1.11 | 0.55 | 1.27 |
| 1 | 1 | 42 | 7 | 827 | 0.05 | 0.76 | 2.67 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 43 | 3 | 556 | 0.02 | -0.45 | -0.78 | 0.82 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 44 | 9 | 194 | 0.03 | -1.30 | -0.78 | -0.65 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 45 | 3 | 275 | 0.05 | -1.03 | 0.09 | -0.82 | -0.72 | -0.61 | 0.05 |
| 1 | 1 | 46 | 4 | 782 | 0.04 | 0.58 | 0.09 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 47 | 9 | 217 | 0.07 | -0.83 | 0.92 | -1.18 | -1.11 | -0.61 | 0.91 |
| 1 | 1 | 48 | 2 | 988 | 0.05 | 2.89 | 0.92 | 3.06 | 1.63 | -0.61 | -1.37 |
| 1 | 1 | 49 | 7 | 506 | 0.04 | -0.09 | -0.78 | 0.33 | 0.06 | -0.61 | 0.83 |
| 1 | 1 | 50 | 4 | 760 | 0.02 | 1.17 | -0.78 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 51 | 4 | 123 | 0.08 | -1.35 | -1.20 | -0.75 | -0.72 | 0.55 | 0.18 |
| 1 | 1 | 52 | 4 | 883 | 0.22 | 2.15 | 0.92 | 0.80 | -0.13 | 0.55 | -1.23 |
| 1 | 1 | 53 | 2 | 285 | 0.01 | -0.97 | -1.20 | -0.68 | 0.06 | -0.61 | 0.16 |
| 1 | 1 | 54 | 9 | 865 | 0.09 | 1.14 | 0.92 | 0.36 | 1.63 | 0.55 | 0.81 |
| 1 | 1 | 55 | 8 | 315 | 0.09 | -1.16 | 0.92 | -0.68 | -0.72 | -0.61 | -0.73 |
| 1 | 1 | 56 | 2 | 608 | 0.05 | -0.11 | 0.09 | 0.82 | 0.06 | -0.61 | 1.27 |
| 1 | 1 | 57 | 5 | 610 | 0.08 | 0.35 | 0.09 | 0.43 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 58 | 1 | 799 | 0 | 0.38 | 0.09 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 59 | 4 | 768 | 0.05 | 1.18 | 0.92 | 0.83 | 0.06 | 0.55 | 0.30 |
| 1 | 1 | 60 | 2 | 588 | 0.12 | 0.80 | -0.78 | -0.29 | 0.06 | 0.55 | 0.70 |
| 1 | 1 | 61 | 4 | 894 | 0.1 | 1.63 | 0.09 | -0.08 | 0.06 | 2.88 | 0.92 |
| 1 | 1 | 62 | 5 | 168 | 0.05 | -0.82 | -0.78 | -0.62 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 63 | 8 | 878 | 0.1 | 1.13 | 0.92 | 0.07 | 0.06 | 2.88 | 0.37 |
| 1 | 1 | 64 | 2 | 912 | 0.01 | 1.34 | 1.38 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 65 | 7 | 448 | 0.11 | 0.12 | 0.09 | -0.08 | -0.72 | -0.61 | 0.61 |
| 1 | 1 | 66 | 3 | 896 | 0.13 | 1.04 | 0.92 | -0.36 | 0.06 | 2.88 | -0.87 |
| 1 | 1 | 67 | 2 | 717 | 0.01 | 0.46 | -1.20 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 68 | 8 | 155 | 0.08 | -1.27 | -0.78 | -0.65 | -0.72 | 0.55 | -0.59 |
| 1 | 1 | 69 | 6 | 803 | 0.09 | 0.79 | -0.92 | 0.46 | 1.63 | -0.61 | -1.67 |
| 1 | 1 | 70 | 7 | 622 | 0.1 | 0.97 | 0.92 | -0.58 | -0.72 | 0.55 | 0.39 |
| 1 | 1 | 71 | 3 | 328 | 0.04 | -1.27 | 0.09 | -0.08 | -0.72 | -0.61 | 0.44 |
| 1 | 1 | 72 | 6 | 505 | 0.04 | -0.17 | -0.78 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 73 | 3 | 477 | 0.03 | -0.43 | -0.78 | -0.08 | 0.06 | 0.55 | 0.91 |
| 1 | 1 | 74 | 3 | 696 | 0.01 | -0.04 | -0.78 | 0.82 | 1.63 | -0.61 | 0.47 |
| 1 | 1 | 75 | 6 | 998 | 0.26 | 1.67 | -0.07 | 3.06 | 3.19 | 2.88 | -0.99 |
| 1 | 1 | 76 | 7 | 636 | 0.05 | 0.00 | 0.92 | -0.08 | 0.06 | 0.55 | 0.28 |
| 1 | 1 | 77 | 5 | 344 | 0.04 | -0.63 | 0.92 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 78 | 3 | 895 | 0.01 | 0.13 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 79 | 5 | 755 | 0.04 | 0.14 | -1.20 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 80 | 2 | 367 | 0.01 | 0.22 | -0.78 | -0.69 | 0.06 | -0.61 | -2.23 |
| 1 | 1 | 81 | 3 | 138 | 0.02 | -0.61 | -0.78 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 82 | 3 | 558 | 0.03 | 0.38 | 0.09 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 83 | 7 | 642 | 0.07 | -0.47 | 0.92 | 0.32 | 0.06 | 0.55 | -0.46 |
| 1 | 1 | 84 | 5 | 667 | 0.04 | -0.34 | 0.92 | 0.30 | 0.06 | 0.55 | -1.30 |
| 1 | 1 | 85 | 3 | 983 | 0.07 | 1.52 | 0.92 | 3.06 | 3.19 | 0.55 | 0.08 |
| 1 | 1 | 86 | 4 | 885 | 0.07 | 2.73 | 0.92 | 0.81 | 0.06 | 0.55 | 0.10 |
| 1 | 1 | 87 | 6 | 162 | 0.04 | -1.28 | -0.78 | -0.97 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 88 | 5 | 519 | 0.12 | -0.88 | 0.92 | 0.16 | -0.72 | 0.55 | -0.56 |
| 1 | 1 | 89 | 8 | 848 | 0.19 | -0.02 | 0.61 | 3.06 | 0.06 | -0.61 | -0.90 |
| 1 | 1 | 90 | 3 | 569 | 0.06 | 0.05 | 0.92 | -0.23 | 0.06 | -0.61 | 0.80 |
| 1 | 1 | 91 | 8 | 274 | 0.04 | -0.48 | -0.78 | -0.65 | -0.72 | -0.61 | 0.90 |
| 1 | 1 | 92 | 2 | 511 | 0.06 | -0.96 | 0.09 | -0.08 | 0.06 | 0.55 | 0.08 |
| 1 | 1 | 93 | 1 | 713 | 0 | -0.13 | -1.20 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 94 | 5 | 644 | 0.04 | 0.36 | 0.92 | 0.33 | 0.06 | -0.61 | 0.81 |
| 1 | 1 | 95 | 4 | 626 | 0.09 | -0.83 | 0.92 | 0.23 | 0.06 | 0.55 | -0.59 |
| 1 | 1 | 96 | 2 | 5 | 0.09 | -1.04 | -0.78 | -0.60 | -0.91 | 2.88 | -0.73 |
| 1 | 1 | 97 | 6 | 785 | 0.04 | 1.23 | -0.78 | 0.82 | 1.63 | -0.61 | 0.56 |
| 1 | 1 | 98 | 4 | 38 | 0.05 | -1.71 | -1.20 | -1.18 | -1.11 | -0.61 | 0.83 |
| 1 | 1 | 99 | 3 | 614 | 0.04 | 0.33 | 0.09 | -0.11 | 0.06 | 0.55 | 0.12 |
| 1 | 1 | 100 | 4 | 571 | 0.05 | 0.24 | -0.78 | 0.86 | 0.06 | -0.61 | 0.20 |
| 1 | 1 | 101 | 6 | 847 | 0.04 | 1.13 | 0.92 | 0.82 | 1.63 | -0.61 | 0.37 |
| 1 | 1 | 102 | 5 | 211 | 0.01 | -1.16 | -0.78 | -0.69 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 103 | 4 | 195 | 0.04 | -0.42 | -1.20 | -1.12 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 104 | 5 | 227 | 0.02 | -0.79 | -0.78 | -0.89 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 105 | 5 | 567 | 0.08 | 0.14 | -0.78 | 0.82 | 0.06 | -0.61 | -0.55 |
| 1 | 1 | 106 | 9 | 765 | 0.11 | 0.17 | 2.67 | 0.26 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 107 | 3 | 750 | 0.03 | 0.57 | -0.78 | 0.84 | 1.63 | -0.61 | 0.88 |
| 1 | 1 | 108 | 3 | 348 | 0.04 | -0.94 | 0.09 | -0.08 | -0.72 | -0.61 | -0.56 |
| 1 | 1 | 109 | 5 | 612 | 0.04 | 0.56 | 0.09 | 0.33 | 0.06 | -0.61 | 0.58 |
| 1 | 1 | 110 | 4 | 805 | 0.01 | 0.35 | 2.67 | 0.31 | 0.06 | 0.55 | -0.30 |
| 1 | 1 | 111 | 4 | 797 | 0.1 | 0.20 | -0.99 | 0.82 | 1.63 | 0.55 | 1.27 |
| 1 | 1 | 112 | 10 | 232 | 0.02 | -0.94 | -0.78 | -0.68 | -0.72 | -0.61 | 0.43 |
| 1 | 1 | 113 | 3 | 921 | 0.01 | 0.97 | 2.67 | 0.82 | 1.63 | -0.61 | 0.47 |
| 1 | 1 | 114 | 8 | 899 | 0.11 | 0.97 | 0.97 | 0.69 | 1.63 | 0.55 | 1.52 |
| 1 | 1 | 115 | 2 | 473 | 0.07 | -0.92 | 0.92 | -0.38 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 116 | 3 | 304 | 0.09 | -0.84 | 0.09 | -0.75 | -0.72 | 0.55 | -1.07 |
| 1 | 1 | 117 | 7 | 904 | 0.06 | 1.57 | 0.92 | 0.84 | 1.63 | 0.55 | 0.76 |
| 1 | 1 | 118 | 4 | 637 | 0.06 | 0.31 | -0.78 | 0.89 | 0.06 | 0.55 | 0.45 |
| 1 | 1 | 119 | 3 | 116 | 0.04 | -1.71 | -1.20 | -0.68 | -0.72 | -0.61 | 0.19 |
| 1 | 1 | 120 | 5 | 602 | 0.05 | -0.53 | 0.92 | -0.08 | 0.06 | 0.55 | 0.46 |
| 1 | 1 | 121 | 7 | 354 | 0.11 | -0.09 | 0.09 | -0.70 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 122 | 7 | 456 | 0.04 | -0.38 | -1.20 | 0.33 | 0.06 | -0.61 | 0.79 |
| 1 | 1 | 123 | 2 | 889 | 0.05 | 1.21 | 0.09 | 0.46 | 1.63 | 0.55 | -1.37 |
| 1 | 1 | 124 | 3 | 963 | 0.01 | 1.17 | -0.78 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 125 | 4 | 494 | 0.06 | -0.24 | -1.20 | 0.45 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 126 | 3 | 84 | 0.01 | -0.46 | -0.78 | -0.50 | -0.72 | -0.61 | -2.35 |
| 1 | 1 | 127 | 4 | 65 | 0.06 | -0.89 | 0.09 | -1.18 | -1.11 | -0.61 | -1.37 |
| 1 | 1 | 128 | 8 | 169 | 0.05 | -0.74 | -1.20 | -1.09 | -0.72 | -0.61 | 0.55 |
| 1 | 1 | 129 | 4 | 438 | 0.07 | -0.64 | -1.20 | 0.45 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 130 | 11 | 140 | 0.04 | -1.11 | -0.78 | -0.67 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 131 | 8 | 743 | 0.04 | 0.62 | -0.78 | 0.82 | 1.63 | -0.61 | 0.45 |
| 1 | 1 | 132 | 4 | 855 | 0.11 | 1.17 | 0.09 | 0.73 | 1.63 | 0.55 | -0.02 |
| 1 | 1 | 133 | 4 | 326 | 0.03 | -0.89 | -0.78 | 0.33 | -0.72 | -0.61 | -0.33 |
| 1 | 1 | 134 | 7 | 322 | 0.05 | -0.03 | -0.78 | -0.60 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 135 | 1 | 133 | 0 | -0.82 | -1.20 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 136 | 7 | 418 | 0.1 | 0.10 | 0.92 | -0.75 | -0.72 | -0.61 | -1.25 |
| 1 | 1 | 137 | 5 | 655 | 0.04 | -0.11 | 0.92 | 0.32 | 0.06 | 0.55 | 0.43 |
| 1 | 1 | 138 | 7 | 427 | 0.05 | -0.41 | -0.78 | -0.08 | 0.06 | -0.61 | 0.93 |
| 1 | 1 | 139 | 2 | 813 | 0.01 | 0.58 | 0.09 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 140 | 4 | 176 | 0.02 | -0.49 | -0.78 | -1.18 | -1.11 | -0.61 | 0.07 |
| 1 | 1 | 141 | 6 | 13 | 0.07 | -0.97 | -0.99 | -0.89 | -0.72 | 0.55 | -2.35 |
| 1 | 1 | 142 | 3 | 752 | 0.02 | 0.60 | -1.20 | 0.82 | 1.63 | -0.61 | 0.58 |
| 1 | 1 | 143 | 5 | 570 | 0.1 | 0.07 | 0.92 | -0.39 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 144 | 1 | 810 | 0 | 1.55 | -0.78 | 0.82 | 1.63 | -0.61 | 0.24 |
| 1 | 1 | 145 | 6 | 673 | 0.07 | 0.30 | 0.92 | 0.82 | 0.06 | -0.61 | -0.59 |
| 1 | 1 | 146 | 8 | 767 | 0.03 | 0.91 | -0.78 | 0.82 | 1.63 | -0.61 | 0.58 |
| 1 | 1 | 147 | 2 | 114 | 0.02 | -0.81 | -1.20 | -1.18 | -1.11 | -0.61 | 0.37 |
| 1 | 1 | 148 | 8 | 196 | 0.03 | -0.92 | -1.20 | -0.68 | -0.72 | -0.61 | 0.29 |
| 1 | 1 | 149 | 4 | 340 | 0.09 | -1.30 | -0.99 | -0.08 | 0.06 | -0.61 | -0.11 |
| 1 | 1 | 150 | 7 | 759 | 0.08 | 0.63 | -0.96 | 0.82 | 1.63 | -0.61 | -0.42 |
| 1 | 1 | 151 | 7 | 631 | 0.08 | 0.03 | 0.92 | 0.40 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 152 | 5 | 479 | 0.05 | -0.17 | 0.92 | -0.08 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 153 | 2 | 449 | 0.04 | -1.30 | 0.09 | 0.90 | -0.72 | 0.55 | -1.07 |
| 1 | 1 | 154 | 5 | 265 | 0.06 | -1.09 | -0.78 | -0.08 | -0.72 | -0.61 | -0.49 |
| 1 | 1 | 155 | 1 | 990 | 0 | 2.90 | 0.92 | 0.32 | 4.76 | 0.55 | 0.50 |
| 1 | 1 | 156 | 5 | 319 | 0.06 | -1.03 | -0.78 | -0.08 | -0.72 | 0.55 | 0.23 |
| 1 | 1 | 157 | 6 | 222 | 0.1 | -0.96 | 0.09 | -0.71 | -0.72 | -0.61 | -1.27 |
| 1 | 1 | 158 | 7 | 706 | 0.08 | 0.20 | 0.92 | 0.86 | 0.06 | 0.55 | -0.19 |
| 1 | 1 | 159 | 2 | 458 | 0.04 | -0.63 | 0.09 | 0.30 | -0.72 | 0.55 | 1.27 |
| 1 | 1 | 160 | 3 | 268 | 0.02 | -0.83 | -0.78 | -0.08 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 161 | 9 | 331 | 0.08 | -0.26 | -0.78 | -0.63 | -0.72 | 0.55 | 0.80 |
| 1 | 1 | 162 | 4 | 440 | 0.06 | 0.06 | 0.09 | -0.08 | -0.72 | -0.61 | 1.27 |
| 1 | 1 | 163 | 5 | 390 | 0.09 | 0.43 | -0.87 | -0.59 | 0.06 | -0.61 | -2.38 |
| 1 | 1 | 164 | 5 | 801 | 0.04 | 1.28 | 0.09 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 165 | 7 | 977 | 0.27 | 1.37 | 2.67 | 0.68 | 1.63 | 2.88 | 0.55 |
| 1 | 1 | 166 | 3 | 225 | 0.08 | -0.63 | 0.92 | -1.15 | -0.98 | -0.61 | -1.08 |
| 1 | 1 | 167 | 4 | 970 | 0.01 | 1.30 | -0.78 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 168 | 3 | 902 | 0.15 | 1.72 | -0.49 | -0.08 | 1.63 | -0.61 | -2.39 |
| 1 | 1 | 169 | 4 | 103 | 0.03 | -1.60 | -1.20 | -0.89 | -0.72 | -0.61 | 0.60 |
| 1 | 1 | 170 | 2 | 431 | 0.04 | -0.97 | 0.09 | -0.08 | 0.06 | -0.61 | 1.27 |
| 1 | 1 | 171 | 7 | 19 | 0.19 | -1.24 | -0.96 | -1.20 | -1.05 | 0.55 | -1.33 |
| 1 | 1 | 172 | 6 | 150 | 0.09 | -0.38 | -0.92 | -0.68 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 173 | 7 | 173 | 0.03 | -0.68 | -1.20 | -1.12 | -0.72 | -0.61 | 0.16 |
| 1 | 1 | 174 | 7 | 422 | 0.11 | -0.07 | 0.09 | -0.67 | -0.72 | 0.55 | 0.43 |
| 1 | 1 | 175 | 3 | 806 | 0.05 | 0.60 | 2.67 | 0.86 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 176 | 9 | 826 | 0.14 | 0.58 | 0.92 | -0.12 | 0.06 | 2.88 | 0.30 |
| 1 | 1 | 177 | 5 | 95 | 0.07 | -0.98 | -0.78 | -1.18 | -1.11 | 0.55 | 0.62 |
| 1 | 1 | 178 | 2 | 726 | 0.01 | 0.08 | -1.20 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 179 | 5 | 455 | 0.03 | -0.48 | -0.78 | 0.32 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 180 | 6 | 224 | 0.09 | -1.41 | 0.09 | -0.65 | -0.72 | -0.61 | -0.56 |
| 1 | 1 | 181 | 7 | 886 | 0.05 | 0.50 | -0.78 | 1.73 | 1.63 | 0.55 | -0.68 |
| 1 | 1 | 182 | 3 | 583 | 0.06 | 0.49 | 0.09 | -0.11 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 183 | 3 | 897 | 0.01 | 0.33 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 184 | 5 | 851 | 0.1 | 1.04 | 0.09 | 0.83 | 1.63 | 0.55 | 0.63 |
| 1 | 1 | 185 | 5 | 26 | 0.09 | -1.34 | -1.20 | -1.01 | -0.87 | -0.61 | -1.67 |
| 1 | 1 | 186 | 6 | 2 | 0.1 | -1.48 | -1.13 | -1.29 | -1.11 | -0.61 | -2.29 |
| 1 | 1 | 187 | 3 | 462 | 0.03 | 0.37 | 0.92 | -0.68 | -0.72 | -0.61 | 0.54 |
| 1 | 1 | 188 | 8 | 59 | 0.05 | -1.52 | -1.20 | -1.18 | -1.11 | -0.61 | 0.40 |
| 1 | 1 | 189 | 6 | 428 | 0.11 | 0.86 | -0.78 | -0.24 | -0.72 | -0.61 | 0.66 |
| 1 | 1 | 190 | 1 | 719 | 0 | -0.14 | 2.67 | 0.33 | -0.72 | 0.55 | -0.30 |
| 1 | 1 | 191 | 8 | 424 | 0.07 | -0.64 | 0.92 | -0.60 | -0.72 | 0.55 | 0.69 |
| 1 | 1 | 192 | 6 | 352 | 0.04 | -0.33 | 0.09 | -0.68 | -0.72 | -0.61 | 0.41 |
| 1 | 1 | 193 | 2 | 828 | 0.01 | 0.84 | 0.09 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 194 | 4 | 611 | 0.09 | 0.33 | 0.09 | -0.18 | 0.06 | 0.55 | 0.75 |
| 1 | 1 | 195 | 6 | 537 | 0.09 | 0.15 | 0.92 | -0.55 | -0.72 | 0.55 | 0.18 |
| 1 | 1 | 196 | 7 | 833 | 0.16 | 0.02 | 0.92 | 0.30 | 0.06 | 2.88 | -0.55 |
| 1 | 1 | 197 | 6 | 563 | 0.14 | 0.47 | 0.09 | -0.18 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 198 | 6 | 650 | 0.09 | 0.88 | 0.92 | -0.22 | 0.06 | -0.61 | 0.88 |
| 1 | 1 | 199 | 8 | 942 | 0.19 | 0.81 | 1.03 | 0.59 | 1.63 | 2.88 | 0.08 |
| 1 | 1 | 200 | 5 | 164 | 0.05 | -1.34 | -0.78 | -0.08 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 201 | 4 | 972 | 0.05 | 1.17 | -0.78 | 3.06 | 3.19 | 0.55 | 0.27 |
| 1 | 1 | 202 | 9 | 216 | 0.03 | -1.12 | -0.78 | -0.67 | -0.72 | -0.61 | 0.47 |
| 1 | 1 | 203 | 8 | 351 | 0.09 | -0.79 | 0.09 | -0.61 | -0.72 | 0.55 | 0.74 |
| 1 | 1 | 204 | 5 | 686 | 0.04 | 0.43 | 0.92 | 0.32 | 0.06 | 0.55 | 0.17 |
| 1 | 1 | 205 | 4 | 284 | 0.04 | -0.92 | -0.78 | -0.08 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 206 | 5 | 817 | 0.04 | 1.18 | 0.09 | 0.82 | 1.63 | -0.61 | 0.12 |
| 1 | 1 | 207 | 6 | 547 | 0.04 | 0.39 | -0.78 | 0.33 | 0.06 | -0.61 | 0.56 |
| 1 | 1 | 208 | 5 | 298 | 0.03 | -1.17 | -0.78 | 0.33 | -0.72 | -0.61 | -0.33 |
| 1 | 1 | 209 | 5 | 818 | 0.04 | 0.56 | 0.92 | 0.82 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 210 | 6 | 27 | 0.04 | -0.58 | -1.20 | -1.08 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 211 | 10 | 214 | 0.06 | -0.67 | -1.20 | -0.65 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 212 | 6 | 486 | 0.06 | -0.37 | -0.78 | -0.08 | 0.06 | 0.55 | 0.58 |
| 1 | 1 | 213 | 7 | 188 | 0.05 | -1.10 | -0.78 | -0.68 | -0.72 | -0.61 | -0.53 |
| 1 | 1 | 214 | 3 | 516 | 0.03 | 0.37 | -0.78 | -0.08 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 215 | 6 | 888 | 0.17 | 0.41 | 0.92 | 1.02 | 0.06 | 2.88 | 0.14 |
| 1 | 1 | 216 | 4 | 712 | 0.02 | 0.17 | -0.78 | 0.82 | 1.63 | -0.61 | 0.44 |
| 1 | 1 | 217 | 7 | 613 | 0.07 | 0.57 | 0.09 | 0.33 | 0.06 | -0.61 | 0.13 |
| 1 | 1 | 218 | 4 | 179 | 0.11 | -0.49 | -0.89 | -0.79 | -0.72 | 0.55 | 1.52 |
| 1 | 1 | 219 | 12 | 598 | 0.05 | -0.23 | 0.92 | 0.32 | 0.06 | -0.61 | -0.38 |
| 1 | 1 | 220 | 6 | 518 | 0.03 | 0.04 | -0.78 | 0.33 | 0.06 | -0.61 | 0.04 |
| 1 | 1 | 221 | 7 | 296 | 0.06 | -0.90 | -0.78 | -0.08 | -0.72 | -0.61 | 0.39 |
| 1 | 1 | 222 | 10 | 145 | 0.04 | -1.32 | -0.78 | -0.69 | -0.72 | -0.61 | -0.77 |
| 1 | 1 | 223 | 7 | 681 | 0.04 | 0.30 | 0.92 | 0.34 | 0.06 | 0.55 | 0.40 |
| 1 | 1 | 224 | 8 | 172 | 0.07 | -1.62 | -0.78 | -0.08 | -0.72 | -0.61 | -0.67 |
| 1 | 1 | 225 | 5 | 98 | 0.03 | -1.44 | -1.20 | -1.15 | -0.72 | -0.61 | 0.30 |
| 1 | 1 | 226 | 4 | 200 | 0.03 | -0.75 | -0.78 | -1.12 | -0.72 | -0.61 | 0.66 |
| 1 | 1 | 227 | 6 | 99 | 0.03 | -1.26 | -1.20 | -1.14 | -0.72 | -0.61 | 0.85 |
| 1 | 1 | 228 | 2 | 682 | 0.01 | 0.92 | 0.92 | -0.08 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 229 | 1 | 815 | 0 | 0.47 | 2.67 | 0.30 | 0.06 | 0.55 | -0.30 |
| 1 | 1 | 230 | 1 | 679 | 0 | -0.36 | 0.92 | 1.78 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 231 | 5 | 389 | 0.07 | -0.20 | -0.78 | -0.65 | 0.06 | -0.61 | -0.01 |
| 1 | 1 | 232 | 4 | 695 | 0.16 | -0.17 | -0.78 | 0.24 | 0.06 | 2.88 | -0.45 |
| 1 | 1 | 233 | 3 | 121 | 0 | -1.65 | -1.20 | -0.68 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 234 | 1 | 325 | 0 | 0.21 | -0.78 | -0.89 | 0.06 | -0.61 | -2.29 |
| 1 | 1 | 235 | 9 | 257 | 0.05 | 0.10 | 0.92 | -1.04 | -0.72 | -0.61 | -2.28 |
| 1 | 1 | 236 | 5 | 949 | 0.32 | 1.24 | -0.78 | 4.82 | -0.09 | -0.38 | 0.49 |
| 1 | 1 | 237 | 4 | 846 | 0.08 | 1.01 | 0.92 | -0.07 | 1.63 | 0.55 | -0.32 |
| 1 | 1 | 238 | 8 | 85 | 0.05 | -1.55 | -1.20 | -1.15 | -0.72 | -0.61 | 0.52 |
| 1 | 1 | 239 | 6 | 577 | 0.02 | -0.52 | 0.92 | 0.32 | 0.06 | -0.61 | -0.30 |
| 1 | 1 | 240 | 12 | 57 | 0.04 | -0.55 | -0.78 | -0.89 | -0.72 | -0.61 | -2.30 |
| 1 | 1 | 241 | 5 | 41 | 0.15 | -0.63 | -0.95 | -1.11 | 0.06 | -0.61 | -2.37 |
| 1 | 1 | 242 | 7 | 689 | 0.12 | 0.63 | 0.92 | -0.32 | 0.06 | 0.55 | -1.08 |
| 1 | 1 | 243 | 3 | 676 | 0.04 | 0.82 | 0.92 | 0.33 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 244 | 1 | 403 | 0 | -1.81 | -0.78 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 245 | 9 | 857 | 0.11 | 2.39 | 0.92 | 0.76 | 0.06 | 0.55 | 0.45 |
| 1 | 1 | 246 | 3 | 463 | 0.07 | -0.99 | 0.92 | -0.08 | -0.72 | 0.55 | 0.17 |
| 1 | 1 | 247 | 3 | 930 | 0.05 | 1.16 | 2.67 | 0.82 | 1.63 | -0.61 | -0.40 |
| 1 | 1 | 248 | 2 | 849 | 0.02 | 0.24 | -0.78 | 1.73 | 1.63 | 0.55 | 0.07 |
| 1 | 1 | 249 | 5 | 115 | 0.04 | -1.54 | -1.20 | -0.85 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 250 | 1 | 538 | 0 | 0.07 | -0.78 | 0.90 | -0.72 | 0.55 | 0.63 |
| 1 | 1 | 251 | 3 | 819 | 0.07 | 1.11 | 0.09 | -0.08 | -0.72 | 2.88 | 1.10 |
| 1 | 1 | 252 | 3 | 107 | 0.01 | -1.35 | -1.20 | -1.12 | -0.72 | -0.61 | 0.05 |
| 1 | 1 | 253 | 4 | 192 | 0.06 | -0.63 | 0.09 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 254 | 1 | 24 | 0 | -0.13 | 0.92 | -1.29 | -1.11 | -0.61 | -2.39 |
| 1 | 1 | 255 | 5 | 309 | 0.04 | -0.70 | 0.09 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 256 | 3 | 724 | 0.12 | 0.65 | 0.92 | 0.87 | -0.20 | 0.55 | 0.68 |
| 1 | 1 | 257 | 8 | 446 | 0.1 | -0.59 | -0.78 | 0.28 | 0.06 | -0.61 | -0.32 |
| 1 | 1 | 258 | 2 | 747 | 0.06 | 1.22 | 0.92 | 0.85 | 0.06 | -0.61 | -0.85 |
| 1 | 1 | 259 | 6 | 160 | 0.04 | -1.62 | -0.78 | -0.69 | -0.72 | -0.61 | 0.39 |
| 1 | 1 | 260 | 7 | 413 | 0.05 | -0.12 | 0.92 | -0.63 | -0.72 | -0.61 | 0.79 |
| 1 | 1 | 261 | 5 | 177 | 0.12 | -0.44 | 0.92 | -1.05 | -0.87 | -0.61 | -1.67 |
| 1 | 1 | 262 | 5 | 295 | 0.06 | -1.04 | 0.92 | -1.00 | -0.72 | -0.61 | 0.77 |
| 1 | 1 | 263 | 1 | 355 | 0 | -0.63 | -0.78 | 0.33 | -0.72 | -0.61 | -0.30 |
| 1 | 1 | 264 | 5 | 802 | 0.11 | 1.78 | 0.92 | 0.62 | 0.06 | 0.55 | 0.56 |
| 1 | 1 | 265 | 3 | 777 | 0.04 | 0.96 | -1.20 | 0.82 | 1.63 | -0.61 | 0.33 |
| 1 | 1 | 266 | 4 | 276 | 0.16 | -0.17 | -0.89 | -0.61 | -0.72 | 0.55 | -1.58 |
| 1 | 1 | 267 | 3 | 959 | 0.01 | 0.97 | -0.78 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 268 | 2 | 955 | 0.09 | 1.01 | 2.67 | 1.75 | 1.63 | 0.55 | -0.39 |
| 1 | 1 | 269 | 3 | 137 | 0.05 | -1.75 | -0.78 | -0.70 | -0.72 | -0.61 | -0.05 |
| 1 | 1 | 270 | 6 | 210 | 0.03 | -0.71 | -0.78 | -1.12 | -0.72 | -0.61 | 0.27 |
| 1 | 1 | 271 | 3 | 342 | 0.08 | -0.87 | -0.78 | 0.05 | -0.72 | 0.55 | 0.63 |
| 1 | 1 | 272 | 4 | 124 | 0.03 | -0.81 | -0.78 | -1.18 | -1.11 | 0.55 | 0.11 |
| 1 | 1 | 273 | 5 | 410 | 0.08 | -0.50 | 0.09 | -0.69 | 0.06 | -0.61 | 0.58 |
| 1 | 1 | 274 | 4 | 223 | 0.03 | -0.82 | -0.78 | -0.89 | -0.72 | -0.61 | 0.52 |
| 1 | 1 | 275 | 3 | 165 | 0.01 | -0.64 | -0.78 | -1.18 | -1.11 | -0.61 | 0.24 |
| 1 | 1 | 276 | 4 | 387 | 0.04 | -0.85 | 0.09 | 0.31 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 277 | 11 | 253 | 0.04 | -0.31 | -0.78 | -1.12 | -0.72 | -0.61 | 0.11 |
| 1 | 1 | 278 | 4 | 405 | 0.15 | 0.69 | -0.78 | -0.31 | -0.72 | -0.61 | -0.60 |
| 1 | 1 | 279 | 2 | 167 | 0.01 | -1.32 | -0.78 | -0.89 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 280 | 7 | 738 | 0.12 | 1.05 | 0.92 | 0.46 | 0.06 | 0.55 | 0.76 |
| 1 | 1 | 281 | 7 | 229 | 0.04 | -0.65 | -1.20 | -0.68 | -0.72 | -0.61 | 0.57 |
| 1 | 1 | 282 | 4 | 733 | 0.02 | 0.38 | -1.20 | 0.82 | 1.63 | -0.61 | 0.37 |
| 1 | 1 | 283 | 6 | 461 | 0.06 | -0.76 | 0.92 | 0.32 | -0.72 | -0.61 | -0.26 |
| 1 | 1 | 284 | 1 | 925 | 0 | 2.23 | 0.92 | 0.68 | 1.63 | 0.55 | 0.87 |
| 1 | 1 | 285 | 4 | 690 | 0.04 | 1.17 | 0.92 | 0.33 | 0.06 | -0.61 | 0.37 |
| 1 | 1 | 286 | 5 | 498 | 0.08 | 0.34 | -0.78 | -0.16 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 287 | 6 | 737 | 0.15 | 1.18 | 0.92 | 0.34 | 0.06 | 0.55 | -0.03 |
| 1 | 1 | 288 | 7 | 578 | 0.04 | 0.17 | 0.09 | 0.33 | 0.06 | -0.61 | 0.82 |
| 1 | 1 | 289 | 7 | 62 | 0.04 | -1.25 | -1.20 | -1.18 | -1.11 | -0.61 | 0.86 |
| 1 | 1 | 290 | 6 | 835 | 0.03 | 1.34 | 0.09 | 0.86 | 1.63 | -0.61 | 0.83 |
| 1 | 1 | 291 | 7 | 728 | 0.17 | 0.12 | 0.09 | -0.16 | 0.06 | 2.88 | 0.64 |
| 1 | 1 | 292 | 8 | 416 | 0.06 | -0.08 | 0.92 | -0.70 | -0.72 | -0.61 | 0.38 |
| 1 | 1 | 293 | 3 | 39 | 0.05 | -1.43 | -1.20 | -1.25 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 294 | 10 | 703 | 0.15 | 0.38 | -0.78 | -0.14 | 0.06 | 2.88 | 0.76 |
| 1 | 1 | 295 | 8 | 259 | 0.04 | -0.42 | -0.78 | -0.60 | 0.06 | -0.61 | -2.30 |
| 1 | 1 | 296 | 3 | 280 | 0.13 | -0.70 | 0.92 | -1.02 | -0.98 | 0.55 | -1.35 |
| 1 | 1 | 297 | 7 | 252 | 0.04 | -0.46 | -1.20 | -0.68 | -0.72 | -0.61 | 0.15 |
| 1 | 1 | 298 | 2 | 934 | 0.06 | 1.04 | 2.67 | 0.82 | 1.63 | 0.55 | 0.08 |
| 1 | 1 | 299 | 3 | 112 | 0.02 | -1.20 | -0.78 | -1.18 | -1.11 | -0.61 | 0.05 |
| 1 | 1 | 300 | 4 | 437 | 0.02 | 0.12 | 0.92 | -0.68 | -0.72 | -0.61 | 0.60 |
| 1 | 1 | 301 | 3 | 258 | 0.03 | -1.19 | 0.92 | -0.69 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 302 | 5 | 776 | 0.03 | 0.11 | -0.78 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 303 | 4 | 40 | 0.04 | -0.88 | -0.78 | -0.08 | -0.72 | 2.88 | 0.47 |
| 1 | 1 | 304 | 6 | 141 | 0.05 | -1.34 | -1.20 | -0.65 | -0.72 | -0.61 | 0.74 |
| 1 | 1 | 305 | 11 | 874 | 0.05 | 1.57 | 0.92 | 0.83 | 1.63 | -0.61 | 0.71 |
| 1 | 1 | 306 | 4 | 78 | 0.03 | -0.29 | 0.92 | -1.10 | -0.72 | -0.61 | -2.31 |
| 1 | 1 | 307 | 12 | 860 | 0.08 | 1.43 | 0.92 | 0.67 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 308 | 4 | 368 | 0.07 | -0.79 | 0.09 | -0.08 | -0.72 | -0.61 | 0.18 |
| 1 | 1 | 309 | 9 | 94 | 0.04 | -1.24 | -0.78 | -1.18 | -1.11 | -0.61 | 0.83 |
| 1 | 1 | 310 | 8 | 954 | 0.18 | 1.52 | 0.92 | 0.63 | 1.63 | 2.88 | 1.01 |
| 1 | 1 | 311 | 3 | 143 | 0.03 | -0.78 | -1.20 | -1.14 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 312 | 3 | 442 | 0.05 | -0.30 | 0.09 | 0.32 | -0.72 | -0.61 | 0.37 |
| 1 | 1 | 313 | 4 | 3 | 0.09 | -0.56 | -1.20 | -1.06 | -0.72 | 2.88 | 1.04 |
| 1 | 1 | 314 | 2 | 453 | 0.04 | -0.30 | 1.38 | -0.68 | -0.72 | 0.55 | 1.27 |
| 1 | 1 | 315 | 9 | 249 | 0.03 | -0.80 | -0.78 | -0.63 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 316 | 3 | 909 | 0.03 | 1.89 | 0.92 | 0.45 | 1.63 | 0.55 | 0.85 |
| 1 | 1 | 317 | 2 | 879 | 0.01 | 0.30 | 0.92 | 0.81 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 318 | 3 | 1001 | 0.04 | 5.26 | 0.92 | 4.01 | 4.76 | 2.88 | 0.46 |
| 1 | 1 | 319 | 4 | 316 | 0.03 | -0.49 | 0.92 | -1.18 | -1.11 | -0.61 | 0.05 |
| 1 | 1 | 320 | 8 | 218 | 0.05 | -0.76 | -1.20 | -0.68 | -0.72 | -0.61 | 0.10 |
| 1 | 1 | 321 | 3 | 441 | 0.06 | -0.28 | 0.09 | 0.32 | -0.72 | -0.61 | 0.85 |
| 1 | 1 | 322 | 2 | 616 | 0.02 | -0.85 | 0.92 | 0.87 | 0.06 | -0.61 | -0.73 |
| 1 | 1 | 323 | 6 | 358 | 0.04 | -0.24 | 0.09 | -0.68 | -0.72 | -0.61 | 0.79 |
| 1 | 1 | 324 | 4 | 324 | 0.05 | -0.68 | 0.09 | -0.65 | -0.72 | -0.61 | 0.44 |
| 1 | 1 | 325 | 6 | 796 | 0.07 | 1.12 | -0.85 | 0.82 | 1.63 | -0.61 | -0.44 |
| 1 | 1 | 326 | 2 | 841 | 0.07 | 0.22 | 1.15 | -0.08 | 1.63 | 0.55 | 1.52 |
| 1 | 1 | 327 | 6 | 435 | 0.03 | 0.13 | 0.92 | -0.68 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 328 | 5 | 147 | 0.04 | -1.18 | -1.20 | -0.85 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 329 | 4 | 951 | 0.12 | 2.65 | 0.92 | 0.72 | 0.06 | 2.88 | 1.04 |
| 1 | 1 | 330 | 5 | 804 | 0.08 | 1.22 | 0.92 | -0.08 | 0.06 | 0.55 | -2.18 |
| 1 | 1 | 331 | 5 | 770 | 0.04 | 0.48 | -0.78 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 332 | 4 | 302 | 0.05 | 0.44 | -0.78 | -0.69 | -0.72 | 2.88 | 0.89 |
| 1 | 1 | 333 | 1 | 198 | 0 | -0.30 | -0.78 | -1.18 | -1.11 | -0.61 | 0.24 |
| 1 | 1 | 334 | 6 | 207 | 0.06 | -1.23 | 0.09 | -1.01 | -0.72 | -0.61 | 0.75 |
| 1 | 1 | 335 | 2 | 4 | 0.02 | -1.31 | -0.78 | -0.50 | -1.11 | 2.88 | 0.07 |
| 1 | 1 | 336 | 6 | 135 | 0.02 | -0.96 | -0.78 | -1.18 | -1.11 | -0.61 | 0.05 |
| 1 | 1 | 337 | 4 | 202 | 0.04 | -0.77 | -1.20 | -0.68 | -0.72 | 0.55 | 0.82 |
| 1 | 1 | 338 | 1 | 504 | 0 | -0.95 | 0.92 | 0.87 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 339 | 4 | 836 | 0.04 | 0.75 | 0.92 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 340 | 6 | 709 | 0.1 | 1.16 | 0.92 | -0.16 | 0.06 | 0.55 | 0.30 |
| 1 | 1 | 341 | 7 | 566 | 0.1 | -0.55 | -0.78 | 0.82 | 0.06 | 0.55 | -0.88 |
| 1 | 1 | 342 | 7 | 565 | 0.02 | -0.54 | 0.92 | 0.31 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 343 | 9 | 892 | 0.1 | 0.53 | 2.67 | 0.75 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 344 | 3 | 347 | 0.04 | -1.03 | 0.92 | -0.57 | -0.72 | -0.61 | 0.49 |
| 1 | 1 | 345 | 3 | 101 | 0.06 | -1.14 | -1.20 | -1.04 | -0.72 | 0.55 | 0.59 |
| 1 | 1 | 346 | 2 | 843 | 0.04 | 0.54 | 0.09 | 0.82 | 1.63 | 0.55 | 1.27 |
| 1 | 1 | 347 | 6 | 166 | 0.04 | -0.93 | -0.78 | -1.13 | -0.72 | -0.61 | 0.88 |
| 1 | 1 | 348 | 4 | 867 | 0.07 | 0.71 | 0.92 | 0.32 | 0.06 | 2.88 | 0.73 |
| 1 | 1 | 349 | 8 | 595 | 0.06 | 0.19 | 0.92 | -0.57 | 0.06 | -0.61 | -2.28 |
| 1 | 1 | 350 | 8 | 69 | 0.1 | -0.76 | 1.15 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 351 | 8 | 800 | 0.15 | 0.53 | -0.94 | 0.77 | 1.63 | 0.55 | -0.22 |
| 1 | 1 | 352 | 3 | 554 | 0.07 | 0.40 | 0.09 | -0.08 | -0.72 | 0.55 | 1.10 |
| 1 | 1 | 353 | 4 | 546 | 0.07 | -0.23 | 0.09 | 0.33 | 0.06 | -0.61 | 0.95 |
| 1 | 1 | 354 | 8 | 771 | 0.11 | -0.22 | 0.92 | 1.77 | 0.06 | 0.55 | -0.96 |
| 1 | 1 | 355 | 6 | 483 | 0.05 | -0.23 | -1.20 | 0.33 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 356 | 3 | 939 | 0.19 | 1.90 | 0.08 | 0.87 | 0.06 | 2.88 | -1.08 |
| 1 | 1 | 357 | 9 | 982 | 0.38 | 3.40 | 0.08 | 3.38 | 1.63 | -0.61 | 0.74 |
| 1 | 1 | 358 | 2 | 597 | 0 | -0.46 | 0.92 | 0.30 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 359 | 5 | 499 | 0.03 | -0.56 | -0.78 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 360 | 3 | 573 | 0.05 | 1.21 | -0.78 | -0.08 | 0.06 | -0.61 | 0.50 |
| 1 | 1 | 361 | 3 | 596 | 0.06 | -1.13 | 0.92 | -0.08 | 0.06 | 0.55 | -0.92 |
| 1 | 1 | 362 | 10 | 649 | 0.08 | 0.07 | 0.92 | 0.38 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 363 | 3 | 392 | 0.02 | -0.66 | -0.78 | -0.56 | 0.06 | 0.55 | 0.87 |
| 1 | 1 | 364 | 5 | 215 | 0.02 | -0.79 | -0.78 | -0.89 | -0.72 | -0.61 | 0.88 |
| 1 | 1 | 365 | 4 | 745 | 0.16 | 1.86 | -0.13 | 0.45 | 0.06 | 0.55 | 0.26 |
| 1 | 1 | 366 | 2 | 76 | 0.03 | -1.68 | -1.20 | -1.17 | -0.72 | -0.61 | 0.16 |
| 1 | 1 | 367 | 9 | 289 | 0.07 | -0.42 | -0.78 | -0.57 | -0.72 | -0.61 | -0.40 |
| 1 | 1 | 368 | 4 | 707 | 0.09 | 0.66 | 0.92 | -0.40 | 0.06 | 0.55 | -1.67 |
| 1 | 1 | 369 | 5 | 300 | 0.05 | -0.19 | -0.78 | -0.65 | -0.72 | -0.61 | 0.90 |
| 1 | 1 | 370 | 2 | 49 | 0.02 | -1.35 | -1.20 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 371 | 11 | 844 | 0.2 | 0.49 | 0.96 | 0.75 | 1.63 | 0.55 | 0.34 |
| 1 | 1 | 372 | 10 | 975 | 0.08 | 1.53 | 0.92 | 3.06 | 3.19 | -0.61 | 0.27 |
| 1 | 1 | 373 | 4 | 887 | 0.09 | 1.87 | 0.92 | 0.78 | 1.63 | -0.61 | 0.26 |
| 1 | 1 | 374 | 3 | 189 | 0.03 | -0.48 | -1.20 | -1.12 | -0.72 | -0.61 | 0.18 |
| 1 | 1 | 375 | 2 | 671 | 0.08 | 1.11 | 0.92 | -0.62 | -0.72 | 0.55 | -1.37 |
| 1 | 1 | 376 | 7 | 648 | 0.06 | -0.39 | 0.92 | 0.82 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 377 | 3 | 472 | 0.09 | 0.10 | -1.06 | 0.03 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 378 | 2 | 294 | 0.03 | -0.80 | -1.20 | -0.68 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 379 | 5 | 412 | 0.1 | 0.86 | -0.78 | -0.36 | -0.72 | -0.61 | 0.13 |
| 1 | 1 | 380 | 4 | 402 | 0.04 | -0.29 | 2.67 | -0.68 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 381 | 8 | 915 | 0.13 | 1.19 | 0.09 | -0.55 | -0.33 | 2.88 | -2.30 |
| 1 | 1 | 382 | 4 | 129 | 0.03 | -1.32 | -0.78 | -1.13 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 383 | 3 | 672 | 0.06 | 0.91 | 0.09 | -0.08 | 0.06 | 0.55 | 1.18 |
| 1 | 1 | 384 | 2 | 1000 | 0.07 | 2.26 | 0.92 | 8.30 | 1.63 | 0.55 | 0.27 |
| 1 | 1 | 385 | 5 | 697 | 0.07 | -0.43 | 0.92 | 0.86 | 0.06 | 0.55 | -1.30 |
| 1 | 1 | 386 | 3 | 46 | 0.03 | -1.26 | -1.20 | -1.18 | -1.11 | 0.55 | 0.12 |
| 1 | 1 | 387 | 5 | 501 | 0.14 | 1.16 | 0.59 | -0.92 | -0.72 | -0.61 | -2.32 |
| 1 | 1 | 388 | 3 | 206 | 0.03 | -0.60 | -0.78 | -1.13 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 389 | 8 | 997 | 0.23 | 2.05 | 2.67 | 3.06 | 3.19 | -0.18 | 0.32 |
| 1 | 1 | 390 | 3 | 620 | 0.07 | 0.15 | 0.09 | 0.03 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 391 | 6 | 151 | 0.04 | -1.27 | -0.78 | -0.89 | -0.72 | -0.61 | 0.96 |
| 1 | 1 | 392 | 3 | 393 | 0.02 | -0.38 | 0.92 | -0.68 | -0.72 | -0.61 | 0.40 |
| 1 | 1 | 393 | 5 | 439 | 0.09 | -0.21 | -0.78 | -0.58 | 0.06 | 0.55 | -0.99 |
| 1 | 1 | 394 | 19 | 792 | 0.15 | 0.60 | -0.91 | 0.83 | 1.63 | 0.55 | 0.57 |
| 1 | 1 | 395 | 7 | 334 | 0.12 | 0.61 | -0.96 | -0.77 | -0.72 | 2.88 | 0.47 |
| 1 | 1 | 396 | 5 | 684 | 0.06 | 0.56 | 0.92 | 0.85 | 0.06 | -0.61 | 0.37 |
| 1 | 1 | 397 | 11 | 250 | 0.03 | -0.76 | -0.78 | -0.67 | -0.72 | -0.61 | 0.31 |
| 1 | 1 | 398 | 4 | 627 | 0.08 | 0.28 | 0.09 | 0.82 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 399 | 5 | 362 | 0.1 | -0.43 | -0.78 | -0.62 | 0.06 | -0.61 | -0.99 |
| 1 | 1 | 400 | 6 | 246 | 0.05 | -0.27 | -0.78 | -1.13 | -0.72 | -0.61 | 0.75 |
| 1 | 1 | 401 | 3 | 287 | 0.07 | -1.23 | -0.92 | 0.32 | -0.72 | -0.61 | 0.59 |
| 1 | 1 | 402 | 3 | 236 | 0.02 | -0.39 | -1.20 | -0.89 | -0.72 | -0.61 | 0.41 |
| 1 | 1 | 403 | 4 | 97 | 0.07 | -1.65 | -1.20 | -0.61 | -0.72 | -0.61 | -0.41 |
| 1 | 1 | 404 | 4 | 944 | 0.18 | 0.31 | 0.92 | 2.10 | 0.06 | 2.88 | -0.90 |
| 1 | 1 | 405 | 2 | 507 | 0.01 | -0.09 | -0.78 | 0.33 | 0.06 | -0.61 | 0.63 |
| 1 | 1 | 406 | 3 | 8 | 0.04 | -0.69 | -0.78 | -0.08 | -0.72 | 2.88 | 1.52 |
| 1 | 1 | 407 | 9 | 305 | 0.05 | -0.59 | -0.78 | -0.63 | -0.72 | 0.55 | 0.44 |
| 1 | 1 | 408 | 5 | 365 | 0.16 | -0.24 | 2.67 | -0.88 | -0.87 | 0.55 | 1.32 |
| 1 | 1 | 409 | 7 | 790 | 0.11 | 0.03 | 0.92 | 0.10 | 0.06 | 2.88 | 0.43 |
| 1 | 1 | 410 | 4 | 267 | 0.06 | -1.50 | -0.78 | -0.08 | -0.72 | 0.55 | 0.27 |
| 1 | 1 | 411 | 4 | 781 | 0.02 | 1.09 | -0.78 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 412 | 6 | 680 | 0.07 | 0.65 | 0.92 | -0.08 | 0.06 | 0.55 | 0.11 |
| 1 | 1 | 413 | 6 | 991 | 0.07 | 2.13 | 0.92 | 3.06 | 3.19 | 0.55 | -0.62 |
| 1 | 1 | 414 | 5 | 961 | 0.18 | 1.57 | 0.92 | 3.22 | 1.63 | 0.55 | 0.79 |
| 1 | 1 | 415 | 4 | 704 | 0.12 | -0.50 | 0.92 | -0.13 | -0.72 | 2.88 | -0.73 |
| 1 | 1 | 416 | 3 | 700 | 0.07 | 0.84 | 0.92 | 0.83 | 0.06 | -0.61 | 0.63 |
| 1 | 1 | 417 | 6 | 623 | 0.03 | -0.40 | 0.92 | 0.84 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 418 | 4 | 927 | 0.06 | 0.99 | 2.67 | 0.82 | 1.63 | -0.61 | -0.21 |
| 1 | 1 | 419 | 7 | 976 | 0.04 | 1.04 | -0.78 | 3.06 | 3.19 | 0.55 | -0.81 |
| 1 | 1 | 420 | 9 | 130 | 0.04 | -0.96 | -0.78 | -1.18 | -1.11 | -0.61 | 0.58 |
| 1 | 1 | 421 | 6 | 288 | 0.04 | -0.07 | -1.20 | -0.53 | 0.06 | -0.61 | -2.29 |
| 1 | 1 | 422 | 4 | 330 | 0.07 | -0.83 | -0.89 | 0.32 | -0.72 | -0.61 | 0.57 |
| 1 | 1 | 423 | 3 | 434 | 0.07 | -1.02 | 0.09 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 424 | 4 | 607 | 0.03 | -0.07 | 0.92 | -0.50 | 0.06 | 0.55 | 0.87 |
| 1 | 1 | 425 | 7 | 718 | 0.06 | 0.47 | 0.92 | 0.87 | 0.06 | 0.55 | 0.37 |
| 1 | 1 | 426 | 5 | 156 | 0.09 | -0.88 | 0.09 | -1.18 | -1.11 | 0.55 | 0.63 |
| 1 | 1 | 427 | 4 | 966 | 0.16 | 2.70 | 2.67 | 1.77 | 0.06 | 0.55 | -0.54 |
| 1 | 1 | 428 | 5 | 871 | 0.04 | 0.56 | 0.92 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 429 | 6 | 421 | 0.09 | -0.70 | 0.92 | -0.63 | -0.72 | 0.55 | 0.03 |
| 1 | 1 | 430 | 8 | 823 | 0.14 | 2.24 | 0.92 | 0.81 | -0.72 | 0.55 | 0.35 |
| 1 | 1 | 431 | 4 | 906 | 0.03 | 0.54 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 432 | 2 | 581 | 0.03 | -0.04 | 0.09 | -0.08 | 0.06 | 0.55 | 0.14 |
| 1 | 1 | 433 | 2 | 774 | 0.05 | 1.01 | -0.78 | -0.05 | 1.63 | 0.55 | 0.87 |
| 1 | 1 | 434 | 4 | 962 | 0.14 | 2.04 | 0.92 | 0.61 | 1.63 | 2.88 | -0.41 |
| 1 | 1 | 435 | 8 | 947 | 0.04 | 1.28 | 2.67 | 0.81 | 1.63 | 0.55 | 1.01 |
| 1 | 1 | 436 | 5 | 100 | 0.04 | -1.05 | -0.78 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 437 | 3 | 744 | 0.03 | 0.60 | -0.78 | 0.82 | 1.63 | -0.61 | 0.11 |
| 1 | 1 | 438 | 5 | 838 | 0.16 | 0.69 | -0.43 | -0.53 | 1.63 | 0.55 | -2.12 |
| 1 | 1 | 439 | 4 | 48 | 0.02 | -0.47 | -0.78 | -1.08 | -0.72 | -0.61 | -2.30 |
| 1 | 1 | 440 | 7 | 662 | 0.04 | 0.68 | 0.92 | 0.33 | 0.06 | -0.61 | 0.55 |
| 1 | 1 | 441 | 7 | 731 | 0.06 | -0.28 | 0.92 | 1.78 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 442 | 6 | 161 | 0.04 | -0.62 | -0.78 | -1.18 | -1.11 | -0.61 | 0.56 |
| 1 | 1 | 443 | 5 | 25 | 0.04 | -0.20 | 2.67 | -1.18 | -1.11 | -0.61 | 1.01 |
| 1 | 1 | 444 | 5 | 238 | 0.1 | -0.52 | -0.87 | -1.08 | -0.72 | 0.55 | 0.65 |
| 1 | 1 | 445 | 2 | 911 | 0.06 | 0.38 | 2.67 | 0.34 | 1.63 | 0.55 | 0.08 |
| 1 | 1 | 446 | 6 | 66 | 0.06 | -1.61 | -1.20 | -1.16 | -0.72 | -0.61 | 0.91 |
| 1 | 1 | 447 | 4 | 452 | 0.14 | -0.19 | -0.89 | 0.22 | -0.72 | 2.88 | 0.39 |
| 1 | 1 | 448 | 3 | 926 | 0.07 | 1.51 | 0.92 | 0.89 | 0.06 | 2.88 | 0.46 |
| 1 | 1 | 449 | 5 | 917 | 0.11 | 1.43 | 0.92 | -0.53 | 1.63 | 0.55 | -2.12 |
| 1 | 1 | 450 | 3 | 317 | 0.02 | -0.37 | -0.78 | -0.41 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 451 | 2 | 981 | 0.07 | 2.73 | 0.92 | 4.82 | 0.06 | -0.61 | 0.48 |
| 1 | 1 | 452 | 4 | 658 | 0.06 | 0.87 | 0.09 | -0.25 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 453 | 4 | 247 | 0.06 | -0.50 | -0.78 | -0.94 | -0.72 | -0.61 | 0.63 |
| 1 | 1 | 454 | 3 | 291 | 0.03 | -0.20 | -1.20 | -0.58 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 455 | 8 | 119 | 0.04 | -1.23 | -1.20 | -1.12 | -0.72 | -0.61 | 0.58 |
| 1 | 1 | 456 | 7 | 645 | 0.09 | -0.51 | 0.92 | 0.10 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 457 | 2 | 219 | 0 | -1.07 | -0.78 | -0.68 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 458 | 4 | 72 | 0.06 | -1.27 | -0.78 | -1.18 | -1.11 | 0.55 | 0.26 |
| 1 | 1 | 459 | 3 | 338 | 0.09 | 0.21 | -0.92 | -0.63 | -0.72 | -0.61 | 0.54 |
| 1 | 1 | 460 | 3 | 83 | 0.04 | -1.75 | -1.20 | -0.75 | -0.72 | -0.61 | 0.88 |
| 1 | 1 | 461 | 6 | 163 | 0.06 | -0.94 | -0.78 | -0.78 | -0.72 | -0.61 | -0.96 |
| 1 | 1 | 462 | 17 | 503 | 0.07 | -0.56 | -0.78 | 0.82 | 0.06 | -0.61 | -0.74 |
| 1 | 1 | 463 | 4 | 311 | 0.03 | -0.51 | 0.92 | -1.18 | -1.11 | -0.61 | 0.31 |
| 1 | 1 | 464 | 9 | 549 | 0.1 | -0.15 | -0.92 | 0.33 | 0.06 | 0.55 | 0.80 |
| 1 | 1 | 465 | 6 | 63 | 0.06 | -1.10 | -0.78 | -0.96 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 466 | 6 | 191 | 0.04 | -0.83 | 0.09 | -1.18 | -1.11 | -0.61 | 0.44 |
| 1 | 1 | 467 | 3 | 273 | 0.03 | -0.24 | -0.78 | -0.62 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 468 | 4 | 7 | 0.11 | 0.22 | -0.89 | -0.79 | -0.72 | 2.88 | 1.52 |
| 1 | 1 | 469 | 5 | 635 | 0.06 | 0.20 | 0.09 | 0.33 | 0.06 | 0.55 | 0.81 |
| 1 | 1 | 470 | 3 | 845 | 0.06 | 0.38 | 2.67 | 0.81 | 0.06 | 0.55 | -0.18 |
| 1 | 1 | 471 | 6 | 734 | 0.04 | 0.80 | -0.78 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 472 | 6 | 640 | 0.07 | -0.11 | 0.92 | 0.82 | 0.06 | -0.61 | 0.20 |
| 1 | 1 | 473 | 2 | 370 | 0.06 | -1.47 | 0.92 | -0.08 | -0.72 | -0.61 | -0.27 |
| 1 | 1 | 474 | 4 | 562 | 0.12 | 0.63 | 0.92 | -0.08 | -0.72 | -0.61 | 1.07 |
| 1 | 1 | 475 | 6 | 489 | 0.03 | 0.18 | -0.78 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 476 | 2 | 822 | 0 | 0.58 | 2.67 | 0.30 | 0.06 | 0.55 | -0.30 |
| 1 | 1 | 477 | 2 | 739 | 0.01 | 0.38 | -1.20 | 0.82 | 1.63 | -0.61 | 0.70 |
| 1 | 1 | 478 | 4 | 825 | 0.1 | 1.75 | 0.92 | 0.57 | 0.06 | 0.55 | 1.27 |
| 1 | 1 | 479 | 3 | 487 | 0.05 | -0.19 | 0.92 | -0.63 | -0.72 | 0.55 | -0.50 |
| 1 | 1 | 480 | 2 | 794 | 0.03 | 2.39 | 0.92 | 0.46 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 481 | 4 | 91 | 0.07 | -1.65 | -0.78 | -0.16 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 482 | 3 | 75 | 0.09 | -1.58 | -0.78 | 0.20 | -0.72 | 0.55 | -1.30 |
| 1 | 1 | 483 | 5 | 514 | 0.05 | 0.61 | 0.92 | -0.57 | -0.72 | -0.61 | -0.41 |
| 1 | 1 | 484 | 8 | 544 | 0.11 | 0.19 | -0.83 | -0.34 | 0.06 | 0.55 | -1.67 |
| 1 | 1 | 485 | 2 | 907 | 0.02 | 2.21 | 0.92 | 0.68 | 1.63 | -0.61 | 0.87 |
| 1 | 1 | 486 | 3 | 914 | 0.1 | 0.73 | 0.92 | 0.86 | 0.06 | 2.88 | 1.35 |
| 1 | 1 | 487 | 5 | 464 | 0.13 | 0.59 | -0.78 | -0.28 | -0.72 | 0.55 | 0.24 |
| 1 | 1 | 488 | 6 | 377 | 0.07 | -0.44 | -0.78 | 0.39 | -0.72 | -0.61 | 0.47 |
| 1 | 1 | 489 | 4 | 624 | 0.07 | 0.57 | 0.92 | -0.19 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 490 | 6 | 384 | 0.03 | 0.23 | -0.78 | -0.52 | 0.06 | -0.61 | -2.22 |
| 1 | 1 | 491 | 8 | 496 | 0.08 | -0.13 | 0.92 | -0.60 | -0.72 | 0.55 | 0.53 |
| 1 | 1 | 492 | 9 | 465 | 0.09 | 0.36 | 0.92 | -0.65 | -0.72 | -0.61 | 0.11 |
| 1 | 1 | 493 | 3 | 146 | 0.02 | -1.17 | 0.09 | -1.18 | -1.11 | -0.61 | 0.37 |
| 1 | 1 | 494 | 6 | 652 | 0.04 | 0.18 | 0.92 | -0.08 | 0.06 | 0.55 | 0.63 |
| 1 | 1 | 495 | 2 | 125 | 0.01 | -1.18 | -1.20 | -1.12 | -0.72 | -0.61 | 0.24 |
| 1 | 1 | 496 | 8 | 523 | 0.02 | 0.08 | -0.78 | 0.33 | 0.06 | -0.61 | 0.81 |
| 1 | 1 | 497 | 2 | 529 | 0.03 | -0.63 | 0.92 | -0.08 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 498 | 8 | 6 | 0.04 | -1.20 | -0.78 | -1.29 | -1.11 | -0.61 | -2.28 |
| 1 | 1 | 499 | 4 | 580 | 0.02 | -0.67 | 0.92 | 0.31 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 500 | 3 | 297 | 0.02 | 0.18 | -0.78 | -1.12 | -0.72 | -0.61 | 0.11 |
| 1 | 1 | 501 | 4 | 701 | 0.05 | 0.02 | 0.92 | 0.82 | 0.06 | 0.55 | -0.73 |
| 1 | 1 | 502 | 4 | 603 | 0.07 | 1.09 | 0.92 | -0.19 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 503 | 3 | 117 | 0.03 | -1.61 | -0.78 | -1.08 | -0.72 | -0.61 | 0.71 |
| 1 | 1 | 504 | 8 | 974 | 0.23 | 3.06 | 0.92 | -0.06 | 1.63 | 2.88 | -0.44 |
| 1 | 1 | 505 | 4 | 809 | 0.12 | 1.46 | 0.09 | -0.38 | -0.72 | 2.88 | 0.44 |
| 1 | 1 | 506 | 12 | 540 | 0.1 | -0.24 | -0.92 | 0.33 | 0.06 | 0.55 | 0.32 |
| 1 | 1 | 507 | 5 | 420 | 0.03 | -0.49 | -0.78 | -0.08 | 0.06 | -0.61 | 0.53 |
| 1 | 1 | 508 | 2 | 79 | 0.02 | -2.01 | -0.78 | -0.50 | -1.11 | -0.61 | 0.07 |
| 1 | 1 | 509 | 8 | 508 | 0.02 | -0.07 | -0.78 | 0.33 | 0.06 | -0.61 | 0.42 |
| 1 | 1 | 510 | 12 | 653 | 0.08 | 0.64 | 0.92 | -0.59 | 0.06 | -0.61 | -2.28 |
| 1 | 1 | 511 | 10 | 978 | 0.09 | 1.28 | -0.78 | 3.06 | 3.19 | 0.55 | -0.58 |
| 1 | 1 | 512 | 1 | 356 | 0 | -0.30 | -1.20 | -0.68 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 513 | 7 | 93 | 0.03 | -1.04 | -1.20 | -1.18 | -1.11 | -0.61 | 0.09 |
| 1 | 1 | 514 | 3 | 735 | 0.01 | 0.71 | -1.20 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 515 | 9 | 106 | 0.06 | -0.13 | 0.09 | -1.01 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 516 | 4 | 666 | 0.02 | 0.57 | 0.09 | -0.50 | 0.06 | 0.55 | -2.20 |
| 1 | 1 | 517 | 2 | 346 | 0.07 | -0.37 | -0.78 | 0.34 | -0.72 | -0.61 | -1.37 |
| 1 | 1 | 518 | 4 | 23 | 0.09 | 0.15 | -0.89 | -1.12 | -0.72 | 2.88 | 0.66 |
| 1 | 1 | 519 | 3 | 746 | 0.03 | 0.35 | -1.20 | 0.84 | 1.63 | -0.61 | 0.96 |
| 1 | 1 | 520 | 5 | 70 | 0.08 | -1.34 | -0.87 | -0.69 | -0.72 | 0.55 | 1.52 |
| 1 | 1 | 521 | 7 | 651 | 0.09 | 0.84 | 0.09 | -0.18 | 0.06 | 0.55 | 0.73 |
| 1 | 1 | 522 | 8 | 868 | 0.14 | 0.89 | 0.92 | -0.13 | 0.06 | 2.88 | 0.92 |
| 1 | 1 | 523 | 2 | 933 | 0.01 | 1.30 | 2.67 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 524 | 6 | 756 | 0.1 | 0.68 | -0.92 | 0.46 | 1.63 | -0.61 | -1.08 |
| 1 | 1 | 525 | 5 | 783 | 0.04 | 0.78 | 0.09 | 0.82 | 1.63 | -0.61 | 0.40 |
| 1 | 1 | 526 | 4 | 90 | 0.07 | -1.39 | -0.78 | -0.83 | -0.72 | -0.61 | -1.19 |
| 1 | 1 | 527 | 7 | 665 | 0.1 | -0.14 | -0.78 | 0.04 | 0.06 | 2.88 | 0.57 |
| 1 | 1 | 528 | 5 | 877 | 0.14 | 0.95 | 0.09 | -0.50 | 0.06 | 2.88 | -1.43 |
| 1 | 1 | 529 | 5 | 43 | 0.17 | -0.14 | -0.95 | -0.69 | -0.72 | 2.88 | 0.12 |
| 1 | 1 | 530 | 2 | 572 | 0 | -0.12 | 0.09 | -0.08 | 0.06 | 0.55 | 0.50 |
| 1 | 1 | 531 | 3 | 808 | 0.07 | 1.80 | 0.92 | -0.63 | -0.72 | 0.55 | -2.31 |
| 1 | 1 | 532 | 1 | 568 | 0 | 0.71 | -0.78 | 0.33 | 0.06 | -0.61 | 0.24 |
| 1 | 1 | 533 | 2 | 643 | 0.01 | -0.29 | 0.92 | 0.82 | 0.06 | -0.61 | -0.84 |
| 1 | 1 | 534 | 5 | 467 | 0.08 | -0.85 | -0.78 | 0.24 | 0.06 | 0.55 | -0.33 |
| 1 | 1 | 535 | 7 | 967 | 0.29 | 1.01 | 0.56 | 1.60 | 1.63 | 2.88 | -0.97 |
| 1 | 1 | 536 | 2 | 30 | 0.04 | -2.18 | -0.78 | -1.21 | -1.11 | -0.61 | 0.70 |
| 1 | 1 | 537 | 4 | 178 | 0.07 | -1.25 | -0.78 | -0.66 | -0.72 | 0.55 | 0.85 |
| 1 | 1 | 538 | 6 | 852 | 0.18 | 0.41 | 0.99 | 0.66 | 1.63 | 0.55 | -0.46 |
| 1 | 1 | 539 | 7 | 793 | 0.13 | 1.01 | -0.78 | 0.41 | 1.63 | 0.55 | 0.09 |
| 1 | 1 | 540 | 1 | 971 | 0 | 1.55 | -0.78 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 541 | 4 | 29 | 0.03 | -1.14 | -0.78 | -1.18 | -1.11 | -0.61 | -1.67 |
| 1 | 1 | 542 | 9 | 180 | 0.06 | -1.48 | -0.78 | -0.64 | -0.72 | -0.61 | 0.49 |
| 1 | 1 | 543 | 2 | 104 | 0.01 | -1.81 | -1.20 | -0.68 | -0.72 | -0.61 | 0.42 |
| 1 | 1 | 544 | 1 | 918 | 0 | 2.72 | 0.92 | 0.68 | 1.63 | -0.61 | 0.04 |
| 1 | 1 | 545 | 5 | 372 | 0.04 | -0.71 | 0.09 | -0.08 | -0.72 | -0.61 | 0.63 |
| 1 | 1 | 546 | 5 | 73 | 0.04 | -1.49 | -0.78 | -1.18 | -1.11 | -0.61 | 0.82 |
| 1 | 1 | 547 | 3 | 778 | 0.01 | 1.05 | -0.78 | 0.82 | 1.63 | -0.61 | 0.04 |
| 1 | 1 | 548 | 4 | 842 | 0.12 | 0.83 | 0.09 | 0.11 | 0.06 | 2.88 | 1.04 |
| 1 | 1 | 549 | 4 | 910 | 0.06 | 1.73 | 0.92 | 0.65 | 1.63 | 0.55 | -0.44 |
| 1 | 1 | 550 | 4 | 293 | 0.04 | 0.13 | 0.09 | -0.69 | -0.72 | -0.61 | -2.30 |
| 1 | 1 | 551 | 14 | 816 | 0.15 | 0.43 | 2.67 | 0.47 | 0.06 | 0.55 | 0.51 |
| 1 | 1 | 552 | 2 | 936 | 0.03 | 1.43 | 0.92 | -0.60 | -0.72 | 2.88 | -2.38 |
| 1 | 1 | 553 | 3 | 52 | 0.06 | -1.75 | -0.92 | -0.69 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 554 | 4 | 510 | 0.1 | -0.33 | 0.09 | 0.02 | 0.06 | -0.61 | 0.54 |
| 1 | 1 | 555 | 5 | 203 | 0.03 | -0.58 | -0.78 | -0.76 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 556 | 2 | 730 | 0.01 | 1.29 | 0.92 | 0.32 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 557 | 9 | 875 | 0.11 | 0.77 | 0.97 | 0.82 | 1.63 | 0.55 | 1.01 |
| 1 | 1 | 558 | 1 | 600 | 0 | 0.50 | 0.09 | -0.08 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 559 | 4 | 772 | 0.03 | 0.80 | -1.20 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 560 | 4 | 715 | 0.11 | 1.24 | -0.78 | -0.33 | -0.72 | 2.88 | 0.14 |
| 1 | 1 | 561 | 4 | 485 | 0.09 | -0.77 | -0.89 | 0.32 | 0.06 | 0.55 | 0.95 |
| 1 | 1 | 562 | 4 | 702 | 0.03 | 1.29 | 0.92 | 0.32 | 0.06 | -0.61 | 0.89 |
| 1 | 1 | 563 | 1 | 688 | 0 | 0.87 | 0.09 | -0.89 | 0.06 | 0.55 | -2.29 |
| 1 | 1 | 564 | 5 | 958 | 0.09 | 0.79 | -0.78 | 1.73 | 1.63 | 2.88 | -0.94 |
| 1 | 1 | 565 | 3 | 993 | 0.11 | 1.63 | 0.92 | 3.44 | 1.63 | 2.88 | 0.08 |
| 1 | 1 | 566 | 5 | 586 | 0.05 | -0.41 | 0.09 | 0.82 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 567 | 19 | 471 | 0.13 | 0.23 | -0.78 | -0.64 | 0.06 | 0.55 | -2.31 |
| 1 | 1 | 568 | 2 | 53 | 0.05 | -0.81 | 0.09 | -1.18 | -1.11 | 0.55 | -1.37 |
| 1 | 1 | 569 | 13 | 789 | 0.11 | 0.78 | -0.94 | -0.08 | 1.63 | -0.61 | -2.26 |
| 1 | 1 | 570 | 10 | 985 | 0.06 | 1.28 | -0.78 | 3.06 | 3.19 | 0.55 | -1.30 |
| 1 | 1 | 571 | 8 | 850 | 0.18 | 2.26 | 0.81 | 0.54 | 0.06 | -0.61 | -1.45 |
| 1 | 1 | 572 | 5 | 913 | 0.04 | 0.41 | 0.92 | 1.75 | 1.63 | 0.55 | -0.84 |
| 1 | 1 | 573 | 4 | 552 | 0.07 | -0.20 | -0.89 | 0.33 | 0.06 | 0.55 | -0.41 |
| 1 | 1 | 574 | 3 | 183 | 0.05 | -1.58 | -0.78 | 0.34 | -0.72 | 0.55 | -0.84 |
| 1 | 1 | 575 | 2 | 599 | 0.03 | -0.40 | -0.78 | 0.85 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 576 | 3 | 74 | 0.01 | -0.30 | -0.78 | -0.50 | -0.72 | 0.55 | -2.35 |
| 1 | 1 | 577 | 2 | 787 | 0.01 | 1.53 | 0.92 | 0.81 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 578 | 4 | 282 | 0.08 | -1.29 | 0.09 | -0.55 | -0.72 | -0.61 | 0.30 |
| 1 | 1 | 579 | 3 | 710 | 0.09 | 1.57 | 0.92 | 0.10 | -0.72 | 0.55 | 0.57 |
| 1 | 1 | 580 | 2 | 929 | 0.01 | 1.22 | 2.67 | 0.82 | 1.63 | -0.61 | 0.47 |
| 1 | 1 | 581 | 4 | 791 | 0.01 | 0.38 | -0.78 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 582 | 10 | 995 | 0.08 | 1.99 | 2.67 | 3.06 | 3.19 | -0.61 | -0.11 |
| 1 | 1 | 583 | 7 | 419 | 0.07 | 0.47 | -0.78 | -0.08 | -0.72 | -0.61 | 0.34 |
| 1 | 1 | 584 | 6 | 209 | 0.04 | -0.73 | -1.20 | -0.68 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 585 | 4 | 64 | 0.04 | -1.10 | 0.09 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 586 | 5 | 968 | 0.03 | 1.35 | -0.78 | 3.06 | 3.19 | -0.61 | 0.07 |
| 1 | 1 | 587 | 2 | 10 | 0.03 | -0.89 | -0.78 | -1.29 | -1.11 | -0.61 | -2.28 |
| 1 | 1 | 588 | 2 | 605 | 0.14 | 0.72 | 0.09 | -0.16 | -0.72 | 0.55 | -1.37 |
| 1 | 1 | 589 | 3 | 329 | 0.05 | -1.14 | 0.92 | -0.63 | -0.72 | -0.61 | -0.35 |
| 1 | 1 | 590 | 7 | 957 | 0.29 | 2.55 | -0.78 | -0.06 | 1.63 | 2.88 | -0.28 |
| 1 | 1 | 591 | 3 | 400 | 0.02 | 0.05 | 0.92 | -1.12 | -0.72 | -0.61 | 0.12 |
| 1 | 1 | 592 | 2 | 551 | 0.01 | 0.42 | -0.78 | 0.33 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 593 | 4 | 474 | 0.11 | 0.00 | -0.99 | 0.05 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 594 | 5 | 152 | 0.04 | -0.53 | 1.38 | -1.18 | -1.11 | -0.61 | 1.01 |
| 1 | 1 | 595 | 3 | 964 | 0.27 | 1.63 | 0.92 | 4.82 | -0.20 | -0.23 | 0.50 |
| 1 | 1 | 596 | 3 | 560 | 0.13 | 1.03 | 0.92 | -0.38 | -0.72 | -0.61 | 0.18 |
| 1 | 1 | 597 | 2 | 646 | 0.01 | 0.34 | 0.09 | -0.50 | 0.06 | 0.55 | -2.23 |
| 1 | 1 | 598 | 7 | 109 | 0.04 | -1.23 | -0.78 | -1.18 | -1.11 | -0.61 | 0.39 |
| 1 | 1 | 599 | 5 | 134 | 0.03 | -0.99 | -0.78 | -1.18 | -1.11 | -0.61 | 0.30 |
| 1 | 1 | 600 | 10 | 451 | 0.04 | -0.57 | -0.78 | 0.33 | 0.06 | -0.61 | 0.45 |
| 1 | 1 | 601 | 3 | 395 | 0.13 | -0.94 | 0.92 | -0.48 | -0.72 | 0.55 | 1.35 |
| 1 | 1 | 602 | 14 | 286 | 0.05 | -0.41 | -0.78 | -0.69 | -0.72 | -0.61 | 0.10 |
| 1 | 1 | 603 | 7 | 378 | 0.05 | 0.00 | 0.09 | -0.68 | -0.72 | -0.61 | 0.57 |
| 1 | 1 | 604 | 7 | 621 | 0.14 | -0.69 | 0.09 | 0.70 | 0.06 | 0.55 | -0.87 |
| 1 | 1 | 605 | 6 | 761 | 0.03 | 0.87 | -0.78 | 0.82 | 1.63 | -0.61 | 0.14 |
| 1 | 1 | 606 | 8 | 766 | 0.11 | 0.61 | 2.67 | 0.18 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 607 | 7 | 170 | 0.05 | -1.40 | -0.78 | -0.65 | -0.72 | -0.61 | -0.34 |
| 1 | 1 | 608 | 6 | 773 | 0.02 | 0.92 | -0.78 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 609 | 4 | 32 | 0.04 | -0.92 | -0.78 | -1.10 | -0.72 | -0.61 | -2.31 |
| 1 | 1 | 610 | 10 | 493 | 0.04 | -0.12 | -1.20 | 0.33 | 0.06 | -0.61 | 0.76 |
| 1 | 1 | 611 | 5 | 120 | 0.03 | -1.20 | -0.78 | -0.68 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 612 | 2 | 21 | 0.01 | -1.56 | -1.20 | -1.18 | -1.11 | -0.61 | -1.08 |
| 1 | 1 | 613 | 1 | 615 | 0 | 0.67 | 0.09 | -0.08 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 614 | 4 | 837 | 0.05 | 1.51 | 0.09 | 0.82 | 1.63 | -0.61 | 0.14 |
| 1 | 1 | 615 | 3 | 639 | 0.02 | 0.61 | 0.09 | -0.08 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 616 | 5 | 788 | 0.17 | 0.67 | -0.78 | -0.49 | -0.09 | 2.88 | -1.36 |
| 1 | 1 | 617 | 4 | 840 | 0.06 | 1.13 | 0.09 | 0.46 | 1.63 | -0.61 | -1.37 |
| 1 | 1 | 618 | 6 | 181 | 0.04 | -0.80 | -0.78 | -1.08 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 619 | 5 | 515 | 0.16 | 0.11 | 0.09 | -0.44 | 0.06 | -0.61 | -1.55 |
| 1 | 1 | 620 | 6 | 80 | 0.03 | -1.17 | -1.20 | -1.18 | -1.11 | -0.61 | 0.58 |
| 1 | 1 | 621 | 4 | 530 | 0.11 | -0.06 | 0.92 | -0.61 | 0.06 | -0.61 | 0.22 |
| 1 | 1 | 622 | 3 | 254 | 0.02 | -0.88 | -0.78 | -0.54 | -0.72 | -0.61 | 0.08 |
| 1 | 1 | 623 | 5 | 406 | 0.09 | 0.18 | 0.92 | -1.15 | -0.72 | -0.61 | 0.75 |
| 1 | 1 | 624 | 10 | 685 | 0.07 | -0.09 | 0.92 | 0.87 | 0.06 | 0.55 | 0.50 |
| 1 | 1 | 625 | 9 | 989 | 0.12 | 1.40 | 0.92 | 3.06 | 3.19 | 0.55 | -1.10 |
| 1 | 1 | 626 | 4 | 333 | 0.03 | 0.13 | -0.78 | -0.68 | -0.72 | -0.61 | 0.16 |
| 1 | 1 | 627 | 5 | 901 | 0.14 | 1.42 | 0.92 | 0.15 | 1.63 | 0.55 | -1.20 |
| 1 | 1 | 628 | 1 | 940 | 0 | 0.97 | 2.67 | 0.82 | 1.63 | -0.61 | -1.30 |
| 1 | 1 | 629 | 4 | 266 | 0.04 | -0.83 | -0.78 | -0.69 | -0.72 | 0.55 | 0.06 |
| 1 | 1 | 630 | 1 | 495 | 0 | 0.20 | -0.78 | -0.08 | 0.06 | -0.61 | 0.63 |
| 1 | 1 | 631 | 5 | 525 | 0.03 | 0.09 | -0.78 | 0.33 | 0.06 | -0.61 | 0.27 |
| 1 | 1 | 632 | 4 | 343 | 0.1 | -0.97 | -0.78 | 0.22 | -0.72 | 0.55 | -0.41 |
| 1 | 1 | 633 | 5 | 979 | 0.04 | 1.57 | 0.92 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 634 | 9 | 574 | 0.12 | 0.74 | 0.09 | -0.64 | 0.06 | -0.61 | -2.31 |
| 1 | 1 | 635 | 11 | 821 | 0.16 | 0.15 | 2.67 | 0.22 | 0.06 | 0.55 | 1.43 |
| 1 | 1 | 636 | 6 | 337 | 0.06 | -0.24 | 0.92 | -1.18 | -1.11 | -0.61 | 0.56 |
| 1 | 1 | 637 | 6 | 725 | 0.02 | 0.36 | -0.78 | 0.82 | 1.63 | -0.61 | 0.42 |
| 1 | 1 | 638 | 5 | 459 | 0.1 | -0.99 | 0.92 | 0.55 | -0.72 | -0.61 | -0.75 |
| 1 | 1 | 639 | 2 | 359 | 0.03 | 0.21 | 0.09 | -1.18 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 640 | 7 | 908 | 0.23 | 1.43 | 0.92 | 0.08 | 1.63 | -0.61 | -2.10 |
| 1 | 1 | 641 | 3 | 433 | 0.06 | -0.60 | 0.92 | -0.08 | -0.72 | -0.61 | -0.51 |
| 1 | 1 | 642 | 5 | 654 | 0.07 | 0.93 | 0.09 | -0.22 | 0.06 | 0.55 | 0.22 |
| 1 | 1 | 643 | 3 | 798 | 0.02 | 1.24 | -0.78 | 0.86 | 1.63 | -0.61 | 0.83 |
| 1 | 1 | 644 | 3 | 239 | 0.05 | -1.27 | -0.78 | 0.20 | -0.72 | -0.61 | -0.84 |
| 1 | 1 | 645 | 5 | 92 | 0.07 | -1.19 | -0.78 | -0.73 | -0.72 | 0.55 | -1.26 |
| 1 | 1 | 646 | 7 | 234 | 0.02 | -0.77 | -0.78 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 647 | 6 | 445 | 0.08 | -0.55 | -1.20 | 0.41 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 648 | 3 | 872 | 0.06 | 1.13 | -0.78 | 0.89 | 0.06 | 2.88 | 0.46 |
| 1 | 1 | 649 | 5 | 327 | 0.09 | -0.04 | -1.03 | -0.81 | -0.72 | 0.55 | 0.51 |
| 1 | 1 | 650 | 3 | 834 | 0.07 | 0.62 | 2.67 | 0.82 | 0.06 | -0.61 | 1.18 |
| 1 | 1 | 651 | 4 | 592 | 0.03 | -0.16 | -0.78 | 0.87 | 0.06 | 0.55 | 0.48 |
| 1 | 1 | 652 | 5 | 579 | 0.06 | 0.17 | -0.78 | 0.33 | 0.06 | 0.55 | 0.36 |
| 1 | 1 | 653 | 6 | 349 | 0.05 | -0.59 | -0.78 | -0.63 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 654 | 3 | 128 | 0.06 | -0.68 | -0.92 | -1.11 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 655 | 11 | 870 | 0.05 | 0.16 | -0.78 | 1.73 | 1.63 | 0.55 | -0.74 |
| 1 | 1 | 656 | 9 | 584 | 0.08 | -0.33 | 0.09 | 0.82 | 0.06 | -0.61 | -0.74 |
| 1 | 1 | 657 | 2 | 873 | 0.01 | 1.01 | 0.92 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 658 | 5 | 575 | 0.19 | 0.40 | -0.87 | -0.16 | 0.06 | 0.55 | -0.95 |
| 1 | 1 | 659 | 9 | 374 | 0.06 | -0.06 | -0.78 | -0.08 | -0.72 | -0.61 | 0.53 |
| 1 | 1 | 660 | 4 | 369 | 0.08 | -0.43 | 0.92 | -1.18 | -1.11 | 0.55 | -0.02 |
| 1 | 1 | 661 | 8 | 956 | 0.23 | 1.20 | 0.92 | 3.21 | 1.63 | 0.40 | -0.33 |
| 1 | 1 | 662 | 5 | 230 | 0.05 | -0.76 | -1.20 | -0.68 | -0.72 | 0.55 | 0.25 |
| 1 | 1 | 663 | 5 | 812 | 0.22 | 0.33 | -0.78 | 3.27 | 0.06 | -0.15 | 0.31 |
| 1 | 1 | 664 | 6 | 541 | 0.07 | 0.27 | -0.85 | 0.33 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 665 | 1 | 784 | 0 | 0.13 | 2.67 | 0.30 | 0.06 | 0.55 | -0.30 |
| 1 | 1 | 666 | 4 | 476 | 0.03 | -0.35 | -0.78 | 0.33 | 0.06 | -0.61 | 0.76 |
| 1 | 1 | 667 | 9 | 864 | 0.08 | 1.46 | 0.92 | -0.02 | 1.63 | 0.55 | 0.10 |
| 1 | 1 | 668 | 13 | 903 | 0.11 | 0.52 | 0.09 | 1.73 | 1.63 | 0.55 | -0.93 |
| 1 | 1 | 669 | 2 | 916 | 0.02 | 0.66 | -0.78 | 3.06 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 670 | 2 | 832 | 0.09 | 2.14 | 0.50 | -0.12 | 0.06 | -0.61 | -2.38 |
| 1 | 1 | 671 | 4 | 722 | 0.09 | 2.18 | 0.09 | 0.46 | 0.06 | -0.61 | 0.52 |
| 1 | 1 | 672 | 6 | 42 | 0.06 | -0.35 | -0.78 | -0.69 | -0.72 | 2.88 | 0.60 |
| 1 | 1 | 673 | 5 | 721 | 0.12 | 1.03 | 0.92 | -0.39 | -0.72 | 0.55 | -2.32 |
| 1 | 1 | 674 | 7 | 237 | 0.05 | -0.78 | -0.78 | -0.62 | -0.72 | -0.61 | -0.52 |
| 1 | 1 | 675 | 5 | 231 | 0.06 | -0.52 | -0.78 | -0.66 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 676 | 7 | 199 | 0.03 | -1.15 | -0.78 | -0.68 | -0.72 | -0.61 | 0.76 |
| 1 | 1 | 677 | 6 | 159 | 0.03 | -1.55 | -0.78 | -0.63 | -0.72 | -0.61 | 0.77 |
| 1 | 1 | 678 | 5 | 447 | 0.04 | 0.25 | -0.78 | -0.53 | 0.06 | -0.61 | 0.17 |
| 1 | 1 | 679 | 6 | 429 | 0.11 | -0.67 | 0.09 | 0.05 | -0.72 | 0.55 | 0.55 |
| 1 | 1 | 680 | 5 | 394 | 0.07 | 0.18 | 0.09 | -0.68 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 681 | 5 | 937 | 0.16 | 2.21 | 0.92 | 0.27 | 0.06 | 2.88 | 1.14 |
| 1 | 1 | 682 | 6 | 609 | 0.03 | 0.50 | 0.09 | 0.33 | 0.06 | -0.61 | 0.83 |
| 1 | 1 | 683 | 7 | 532 | 0.14 | -0.40 | 0.92 | -0.02 | -0.72 | 0.55 | 0.50 |
| 1 | 1 | 684 | 4 | 56 | 0.04 | -1.31 | -0.78 | -1.18 | -1.11 | 0.55 | 0.85 |
| 1 | 1 | 685 | 7 | 524 | 0.03 | 0.10 | -0.78 | 0.33 | 0.06 | -0.61 | 0.57 |
| 1 | 1 | 686 | 4 | 320 | 0.1 | -1.30 | -0.99 | -0.08 | 0.06 | -0.61 | 1.27 |
| 1 | 1 | 687 | 8 | 839 | 0.2 | 0.88 | -0.89 | 0.33 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 688 | 3 | 272 | 0.08 | -1.33 | 0.09 | -0.43 | -0.72 | -0.61 | 0.93 |
| 1 | 1 | 689 | 6 | 675 | 0.06 | -0.32 | 0.92 | 0.86 | 0.06 | 0.55 | 0.01 |
| 1 | 1 | 690 | 7 | 58 | 0.05 | -0.25 | -0.78 | -1.08 | -0.72 | -0.61 | -2.24 |
| 1 | 1 | 691 | 4 | 228 | 0.02 | -0.51 | 0.09 | -1.18 | -1.11 | -0.61 | 0.87 |
| 1 | 1 | 692 | 4 | 50 | 0.04 | -1.22 | -0.78 | -1.18 | -1.11 | -0.61 | -1.08 |
| 1 | 1 | 693 | 5 | 444 | 0.12 | 0.57 | -0.78 | -0.44 | -0.72 | 0.55 | -0.52 |
| 1 | 1 | 694 | 9 | 948 | 0.27 | 0.91 | 2.67 | 0.45 | 0.06 | 2.88 | 0.77 |
| 1 | 1 | 695 | 9 | 415 | 0.07 | -0.46 | -0.78 | -0.58 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 696 | 8 | 693 | 0.11 | 0.46 | 0.97 | 0.29 | 0.06 | 0.55 | 0.87 |
| 1 | 1 | 697 | 1 | 663 | 0 | 0.45 | 2.67 | -0.41 | -0.72 | -0.61 | -0.84 |
| 1 | 1 | 698 | 8 | 345 | 0.06 | -0.17 | 0.92 | -1.18 | -1.11 | -0.61 | 0.11 |
| 1 | 1 | 699 | 6 | 244 | 0.04 | -0.48 | 0.09 | -1.18 | -1.11 | -0.61 | 0.58 |
| 1 | 1 | 700 | 3 | 279 | 0.07 | 0.28 | -0.78 | -0.58 | -0.72 | 0.55 | -2.31 |
| 1 | 1 | 701 | 4 | 670 | 0.09 | 0.69 | 0.09 | 0.74 | 0.06 | -0.61 | -1.02 |
| 1 | 1 | 702 | 8 | 361 | 0.06 | -0.82 | 0.92 | -0.69 | -0.72 | -0.61 | 0.09 |
| 1 | 1 | 703 | 3 | 863 | 0.06 | 0.20 | 0.92 | 0.06 | 0.06 | 2.88 | 1.52 |
| 1 | 1 | 704 | 7 | 659 | 0.05 | 0.64 | 0.92 | 0.33 | 0.06 | -0.61 | 0.10 |
| 1 | 1 | 705 | 11 | 633 | 0.1 | -0.52 | 0.92 | 0.32 | 0.06 | 0.55 | 0.23 |
| 1 | 1 | 706 | 4 | 256 | 0.1 | -0.64 | 0.09 | -1.18 | -1.11 | 0.55 | -0.02 |
| 1 | 1 | 707 | 5 | 550 | 0.09 | 0.56 | 0.09 | -0.59 | -0.72 | 0.55 | -2.34 |
| 1 | 1 | 708 | 7 | 780 | 0.19 | 2.21 | 0.92 | 0.69 | -0.16 | -0.61 | 0.09 |
| 1 | 1 | 709 | 2 | 470 | 0.02 | 0.84 | -0.78 | -0.53 | 0.06 | -0.61 | -2.39 |
| 1 | 1 | 710 | 4 | 757 | 0.02 | 0.58 | -1.20 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 711 | 2 | 139 | 0.01 | -0.99 | -1.20 | -1.12 | -0.72 | -0.61 | 0.24 |
| 1 | 1 | 712 | 5 | 51 | 0.05 | -1.70 | -1.20 | -1.17 | -0.72 | -0.61 | -0.48 |
| 1 | 1 | 713 | 8 | 299 | 0.11 | -0.16 | 0.09 | -0.77 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 714 | 8 | 251 | 0.06 | -0.88 | -0.78 | -0.66 | -0.72 | 0.55 | 0.79 |
| 1 | 1 | 715 | 7 | 204 | 0.03 | -0.76 | -0.78 | -1.11 | -0.72 | -0.61 | 0.05 |
| 1 | 1 | 716 | 6 | 466 | 0.1 | -0.44 | 0.92 | 0.14 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 717 | 2 | 89 | 0 | -1.47 | -1.20 | -1.18 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 718 | 6 | 548 | 0.03 | 0.37 | -0.78 | 0.33 | 0.06 | -0.61 | 0.14 |
| 1 | 1 | 719 | 2 | 1 | 0.03 | 0.84 | -0.78 | -0.60 | -0.72 | 2.88 | -2.38 |
| 1 | 1 | 720 | 6 | 31 | 0.08 | -1.48 | -1.20 | -1.18 | -1.11 | 0.55 | 0.69 |
| 1 | 1 | 721 | 2 | 20 | 0.02 | -0.02 | -1.20 | -0.69 | -0.72 | 2.88 | 0.89 |
| 1 | 1 | 722 | 3 | 308 | 0.01 | -0.62 | 0.09 | -0.89 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 723 | 4 | 859 | 0.03 | 1.09 | 0.09 | -0.08 | 1.63 | -0.61 | -2.25 |
| 1 | 1 | 724 | 8 | 619 | 0.18 | -0.15 | 0.09 | 0.28 | 0.06 | 0.55 | 1.27 |
| 1 | 1 | 725 | 3 | 54 | 0.07 | -1.14 | -0.92 | -1.18 | -1.11 | 0.55 | -0.44 |
| 1 | 1 | 726 | 10 | 502 | 0.1 | -0.58 | -0.82 | 0.21 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 727 | 10 | 245 | 0.05 | -0.77 | -0.78 | -0.63 | -0.72 | -0.61 | 0.80 |
| 1 | 1 | 728 | 4 | 144 | 0.05 | -1.55 | -0.78 | -0.61 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 729 | 10 | 105 | 0.06 | -0.14 | -0.78 | -0.63 | -0.72 | -0.61 | -2.33 |
| 1 | 1 | 730 | 5 | 881 | 0.06 | 1.50 | 0.92 | 0.40 | 1.63 | 0.55 | 0.50 |
| 1 | 1 | 731 | 4 | 553 | 0.12 | 0.75 | 0.09 | -0.38 | -0.72 | 0.55 | 0.44 |
| 1 | 1 | 732 | 4 | 527 | 0.02 | 0.50 | -0.78 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 733 | 4 | 996 | 0.16 | 3.40 | 2.67 | 1.77 | 0.06 | 2.88 | -0.54 |
| 1 | 1 | 734 | 8 | 669 | 0.09 | 0.72 | 0.92 | -0.47 | 0.06 | 0.55 | 0.47 |
| 1 | 1 | 735 | 4 | 555 | 0.04 | -0.94 | 0.92 | 0.32 | 0.06 | -0.61 | -0.78 |
| 1 | 1 | 736 | 6 | 751 | 0.17 | 0.38 | 0.50 | 0.19 | -0.72 | 2.88 | 0.46 |
| 1 | 1 | 737 | 4 | 14 | 0.03 | -0.76 | 0.92 | -1.29 | -1.11 | -0.61 | -2.25 |
| 1 | 1 | 738 | 4 | 61 | 0.11 | -0.81 | 1.15 | -1.18 | -1.11 | 0.55 | 1.27 |
| 1 | 1 | 739 | 3 | 318 | 0.06 | 0.49 | 0.09 | -0.82 | -0.72 | -0.61 | -2.35 |
| 1 | 1 | 740 | 5 | 281 | 0.1 | -1.03 | 0.92 | -0.76 | -0.72 | -0.61 | -1.26 |
| 1 | 1 | 741 | 6 | 113 | 0.05 | -1.39 | -1.20 | -0.82 | -0.72 | -0.61 | 0.91 |
| 1 | 1 | 742 | 7 | 754 | 0.08 | 1.56 | 0.92 | 0.83 | 0.06 | -0.61 | 0.74 |
| 1 | 1 | 743 | 7 | 264 | 0.05 | -1.24 | -0.78 | -0.08 | -0.72 | -0.61 | 0.41 |
| 1 | 1 | 744 | 6 | 88 | 0.05 | -1.56 | -0.78 | -0.69 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 745 | 5 | 779 | 0.04 | 0.31 | 2.67 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 746 | 5 | 854 | 0.16 | 2.53 | -0.78 | 0.46 | 0.06 | -0.61 | -2.18 |
| 1 | 1 | 747 | 9 | 226 | 0.04 | -0.94 | -0.78 | -0.65 | -0.72 | -0.61 | 0.81 |
| 1 | 1 | 748 | 5 | 587 | 0.09 | -0.73 | 0.92 | -0.08 | 0.06 | 0.55 | -0.08 |
| 1 | 1 | 749 | 4 | 220 | 0.04 | -1.41 | -0.78 | -0.08 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 750 | 1 | 241 | 0 | -0.23 | -0.78 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 751 | 5 | 807 | 0.04 | 0.56 | 0.92 | 0.82 | 1.63 | -0.61 | 0.47 |
| 1 | 1 | 752 | 7 | 371 | 0.07 | -0.68 | 0.09 | -0.59 | -0.72 | 0.55 | 0.28 |
| 1 | 1 | 753 | 3 | 193 | 0.08 | -1.22 | -0.78 | 0.51 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 754 | 2 | 131 | 0.02 | -1.23 | -0.78 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 755 | 6 | 753 | 0.05 | 0.61 | -1.20 | 0.82 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 756 | 4 | 426 | 0.04 | -0.13 | 2.67 | -0.68 | -0.72 | -0.61 | 0.47 |
| 1 | 1 | 757 | 5 | 277 | 0.05 | -0.75 | 0.92 | -1.18 | -1.11 | -0.61 | 0.45 |
| 1 | 1 | 758 | 10 | 379 | 0.06 | -0.02 | 0.09 | -0.68 | -0.72 | -0.61 | 0.09 |
| 1 | 1 | 759 | 3 | 604 | 0.18 | -0.30 | 2.67 | -0.48 | -0.72 | 0.55 | 0.52 |
| 1 | 1 | 760 | 3 | 323 | 0.07 | -1.27 | 0.92 | -0.54 | -0.72 | -0.61 | 0.85 |
| 1 | 1 | 761 | 3 | 898 | 0.02 | 1.02 | 1.38 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 762 | 4 | 557 | 0.03 | -0.46 | -0.78 | 0.87 | 0.06 | 0.55 | 0.47 |
| 1 | 1 | 763 | 2 | 984 | 0.05 | 4.59 | 0.92 | 0.80 | 0.06 | 2.88 | 0.33 |
| 1 | 1 | 764 | 13 | 260 | 0.08 | -0.45 | 0.09 | -1.18 | -1.11 | -0.61 | 0.10 |
| 1 | 1 | 765 | 4 | 969 | 0.03 | 1.38 | -0.78 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 766 | 2 | 292 | 0.03 | 0.19 | -0.78 | -1.15 | -0.72 | -0.61 | 0.75 |
| 1 | 1 | 767 | 12 | 953 | 0.16 | 2.06 | 0.92 | 0.16 | 1.63 | 2.88 | 0.43 |
| 1 | 1 | 768 | 10 | 576 | 0.04 | -0.59 | 0.92 | 0.31 | 0.06 | -0.61 | -0.68 |
| 1 | 1 | 769 | 1 | 509 | 0 | 1.23 | 0.09 | -0.55 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 770 | 6 | 727 | 0.13 | 0.31 | 2.67 | 0.26 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 771 | 4 | 44 | 0.03 | -1.52 | -1.20 | -1.18 | -1.11 | -0.61 | 0.97 |
| 1 | 1 | 772 | 5 | 531 | 0.1 | 0.18 | 0.92 | -0.65 | -0.72 | 0.55 | 1.01 |
| 1 | 1 | 773 | 4 | 383 | 0.08 | -1.13 | 0.09 | -0.08 | -0.72 | 0.55 | 0.24 |
| 1 | 1 | 774 | 4 | 900 | 0.24 | 0.64 | 0.92 | 3.32 | 0.06 | -0.03 | 0.38 |
| 1 | 1 | 775 | 3 | 417 | 0.06 | 0.05 | -0.78 | -0.58 | 0.06 | -0.61 | 0.79 |
| 1 | 1 | 776 | 6 | 618 | 0.03 | -0.01 | 0.09 | 0.34 | 0.06 | 0.55 | 0.37 |
| 1 | 1 | 777 | 5 | 35 | 0.07 | -0.88 | -0.78 | -0.08 | -0.72 | 2.88 | -0.08 |
| 1 | 1 | 778 | 4 | 388 | 0.07 | -0.43 | -0.78 | 0.02 | -0.72 | 0.55 | 0.36 |
| 1 | 1 | 779 | 6 | 928 | 0.09 | 0.53 | 0.92 | 1.80 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 780 | 4 | 34 | 0.09 | -2.06 | -0.78 | -1.21 | -1.11 | -0.61 | -0.19 |
| 1 | 1 | 781 | 6 | 882 | 0.07 | 0.69 | -0.78 | -0.54 | 0.06 | 2.88 | -2.28 |
| 1 | 1 | 782 | 3 | 397 | 0.06 | 0.24 | 0.09 | -0.65 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 783 | 7 | 153 | 0.02 | -0.73 | -0.78 | -1.18 | -1.11 | -0.61 | 0.06 |
| 1 | 1 | 784 | 1 | 814 | 0 | 0.63 | -0.78 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 785 | 5 | 380 | 0.1 | 0.07 | -0.78 | -0.08 | -0.72 | -0.61 | 1.19 |
| 1 | 1 | 786 | 4 | 866 | 0.11 | -0.40 | 0.30 | 3.06 | 0.06 | 0.55 | -0.84 |
| 1 | 1 | 787 | 6 | 922 | 0.17 | 0.98 | 0.92 | 1.73 | 1.63 | 0.55 | -0.50 |
| 1 | 1 | 788 | 6 | 559 | 0.07 | 0.14 | -0.78 | 0.85 | 0.06 | -0.61 | 0.67 |
| 1 | 1 | 789 | 3 | 213 | 0.01 | -1.16 | -0.78 | -0.68 | -0.72 | -0.61 | 0.37 |
| 1 | 1 | 790 | 5 | 401 | 0.04 | -0.20 | 1.38 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 791 | 3 | 184 | 0 | -0.28 | -0.78 | -1.12 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 792 | 5 | 952 | 0.02 | 1.35 | 2.67 | 0.81 | 1.63 | 0.55 | 1.52 |
| 1 | 1 | 793 | 1 | 890 | 0 | 0.55 | 0.92 | 0.81 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 794 | 3 | 617 | 0.07 | 0.13 | 0.92 | -0.56 | 0.06 | 0.55 | 0.18 |
| 1 | 1 | 795 | 3 | 235 | 0.04 | -0.39 | -1.20 | -0.82 | -0.72 | -0.61 | 0.72 |
| 1 | 1 | 796 | 3 | 534 | 0.01 | 0.21 | -0.78 | 0.33 | 0.06 | -0.61 | 0.80 |
| 1 | 1 | 797 | 4 | 517 | 0.04 | 0.08 | -1.20 | 0.33 | 0.06 | -0.61 | 0.37 |
| 1 | 1 | 798 | 9 | 283 | 0.03 | -0.43 | -0.78 | -0.68 | -0.72 | -0.61 | 0.66 |
| 1 | 1 | 799 | 11 | 593 | 0.09 | -0.20 | 0.92 | 0.28 | 0.06 | -0.61 | 0.46 |
| 1 | 1 | 800 | 7 | 306 | 0.04 | -0.41 | 0.92 | -1.18 | -1.11 | -0.61 | 0.81 |
| 1 | 1 | 801 | 2 | 938 | 0.04 | 1.51 | 2.67 | 1.77 | 0.06 | -0.61 | -1.07 |
| 1 | 1 | 802 | 7 | 876 | 0.15 | 1.43 | 0.92 | 0.71 | 1.63 | -0.61 | -0.62 |
| 1 | 1 | 803 | 7 | 301 | 0.07 | -1.15 | 0.92 | -0.94 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 804 | 7 | 526 | 0.06 | -0.31 | -0.78 | 0.83 | 0.06 | -0.61 | 0.45 |
| 1 | 1 | 805 | 2 | 15 | 0.03 | -0.75 | -0.78 | -0.69 | -0.72 | 2.88 | 1.01 |
| 1 | 1 | 806 | 9 | 262 | 0.03 | -0.60 | -0.78 | -0.67 | -0.72 | -0.61 | 0.78 |
| 1 | 1 | 807 | 3 | 763 | 0.02 | 0.77 | -1.20 | 0.82 | 1.63 | -0.61 | 0.58 |
| 1 | 1 | 808 | 4 | 661 | 0.06 | -0.06 | 0.92 | 0.31 | 0.06 | 0.55 | -0.21 |
| 1 | 1 | 809 | 6 | 22 | 0.03 | -0.89 | -1.20 | -0.96 | -0.72 | -0.61 | -2.36 |
| 1 | 1 | 810 | 2 | 408 | 0.05 | -0.17 | 0.09 | -0.59 | -0.72 | 0.55 | -1.08 |
| 1 | 1 | 811 | 3 | 992 | 0.07 | 2.01 | 2.67 | 3.06 | 1.63 | 0.55 | 1.18 |
| 1 | 1 | 812 | 4 | 45 | 0.04 | -1.27 | -0.78 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 813 | 4 | 932 | 0.13 | 2.77 | 0.92 | 0.48 | 0.06 | 0.55 | -2.36 |
| 1 | 1 | 814 | 3 | 543 | 0.02 | 0.32 | -0.78 | 0.33 | 0.06 | -0.61 | 0.80 |
| 1 | 1 | 815 | 3 | 632 | 0.07 | 0.54 | -0.78 | 0.84 | 0.06 | -0.61 | -1.23 |
| 1 | 1 | 816 | 1 | 945 | 0 | 0.79 | 2.67 | 3.06 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 817 | 5 | 353 | 0.05 | -0.32 | -0.78 | -0.54 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 818 | 5 | 786 | 0.03 | 1.23 | -0.78 | 0.82 | 1.63 | -0.61 | 0.16 |
| 1 | 1 | 819 | 4 | 740 | 0.09 | 0.55 | 0.92 | 0.83 | 0.06 | 0.55 | 1.14 |
| 1 | 1 | 820 | 4 | 386 | 0.04 | -0.42 | 0.92 | -0.64 | -0.72 | -0.61 | 0.82 |
| 1 | 1 | 821 | 7 | 233 | 0.01 | -0.92 | -0.78 | -0.68 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 822 | 5 | 335 | 0.07 | -0.30 | 0.92 | -1.17 | -1.03 | -0.61 | -0.44 |
| 1 | 1 | 823 | 9 | 243 | 0.06 | -1.37 | -0.78 | -0.08 | -0.72 | -0.61 | -0.09 |
| 1 | 1 | 824 | 11 | 691 | 0.12 | 0.44 | 1.38 | 0.42 | 0.06 | -0.61 | 1.29 |
| 1 | 1 | 825 | 3 | 935 | 0.07 | 1.78 | 2.67 | 1.77 | 0.06 | -0.61 | -0.28 |
| 1 | 1 | 826 | 7 | 582 | 0.08 | 0.20 | 0.09 | -0.56 | 0.06 | 0.55 | -1.08 |
| 1 | 1 | 827 | 7 | 350 | 0.06 | -0.66 | -0.78 | -0.61 | 0.06 | -0.61 | 0.09 |
| 1 | 1 | 828 | 3 | 521 | 0.04 | -0.88 | 0.92 | -0.08 | 0.06 | -0.61 | -0.51 |
| 1 | 1 | 829 | 3 | 460 | 0.07 | 0.33 | -0.78 | -0.08 | -0.72 | 0.55 | 1.10 |
| 1 | 1 | 830 | 9 | 185 | 0.02 | -1.37 | -0.78 | -0.69 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 831 | 3 | 376 | 0.06 | -0.52 | 0.09 | -0.63 | -0.72 | 0.55 | -0.50 |
| 1 | 1 | 832 | 7 | 830 | 0.06 | 0.96 | 0.92 | -0.08 | 1.63 | 0.55 | 0.54 |
| 1 | 1 | 833 | 3 | 742 | 0.07 | 1.04 | -0.78 | -0.08 | -0.72 | 2.88 | 1.10 |
| 1 | 1 | 834 | 8 | 186 | 0.03 | -1.15 | -0.78 | -0.67 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 835 | 7 | 668 | 0.19 | 0.88 | 0.92 | -0.22 | 0.06 | -0.61 | -1.25 |
| 1 | 1 | 836 | 3 | 278 | 0.06 | -1.15 | -0.78 | -0.08 | -0.72 | 0.55 | 1.18 |
| 1 | 1 | 837 | 3 | 436 | 0.04 | -0.35 | -0.78 | -0.08 | 0.06 | -0.61 | 0.12 |
| 1 | 1 | 838 | 1 | 303 | 0 | -0.81 | 0.09 | -0.68 | -0.72 | 0.55 | 1.52 |
| 1 | 1 | 839 | 11 | 157 | 0.05 | -1.07 | -1.20 | -0.70 | -0.72 | -0.61 | 0.90 |
| 1 | 1 | 840 | 5 | 950 | 0.04 | 1.48 | 2.67 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 841 | 1 | 999 | 0 | 4.25 | 0.92 | 3.06 | 1.63 | 2.88 | -0.44 |
| 1 | 1 | 842 | 5 | 965 | 0.03 | 1.03 | -0.78 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 843 | 3 | 409 | 0.04 | -0.76 | 0.09 | -0.08 | -0.72 | 0.55 | -0.50 |
| 1 | 1 | 844 | 2 | 77 | 0.02 | -0.93 | -1.20 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 845 | 6 | 385 | 0.08 | -0.21 | 1.23 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 846 | 2 | 122 | 0.03 | -1.23 | -1.20 | -0.68 | -0.72 | 0.55 | 0.89 |
| 1 | 1 | 847 | 8 | 656 | 0.11 | 0.98 | 0.92 | -0.12 | 0.06 | -0.61 | 0.11 |
| 1 | 1 | 848 | 4 | 657 | 0.04 | -0.13 | 2.67 | 0.33 | -0.72 | -0.61 | -0.30 |
| 1 | 1 | 849 | 6 | 321 | 0.13 | -1.33 | -0.92 | -0.08 | 0.06 | -0.61 | -0.92 |
| 1 | 1 | 850 | 5 | 17 | 0.08 | -0.84 | 0.09 | -1.29 | -1.11 | -0.61 | -2.28 |
| 1 | 1 | 851 | 11 | 862 | 0.08 | 1.17 | 0.92 | 0.37 | 1.63 | 0.55 | 0.29 |
| 1 | 1 | 852 | 5 | 261 | 0.07 | -0.87 | 0.09 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 853 | 7 | 564 | 0.07 | 0.08 | 0.09 | 0.33 | 0.06 | -0.61 | 0.32 |
| 1 | 1 | 854 | 5 | 634 | 0.07 | -0.50 | 0.92 | 0.24 | 0.06 | 0.55 | 0.96 |
| 1 | 1 | 855 | 7 | 861 | 0.04 | 1.29 | 0.92 | 0.83 | 1.63 | -0.61 | 0.76 |
| 1 | 1 | 856 | 1 | 407 | 0 | 0.03 | 0.09 | -0.68 | -0.72 | 0.55 | -1.67 |
| 1 | 1 | 857 | 5 | 126 | 0.02 | -1.12 | -1.20 | -1.12 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 858 | 5 | 127 | 0.02 | -1.55 | -0.78 | -1.08 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 859 | 5 | 960 | 0.03 | 1.03 | -0.78 | 3.06 | 3.19 | -0.61 | 0.07 |
| 1 | 1 | 860 | 6 | 201 | 0.09 | -0.74 | -0.92 | -0.55 | -0.72 | -0.61 | -1.04 |
| 1 | 1 | 861 | 2 | 924 | 0.06 | 2.44 | 0.92 | 0.57 | 1.63 | 0.55 | 0.04 |
| 1 | 1 | 862 | 1 | 893 | 0 | 1.05 | 2.67 | 0.82 | 0.06 | 0.55 | -0.30 |
| 1 | 1 | 863 | 4 | 86 | 0.02 | -1.50 | -0.78 | -1.18 | -1.11 | -0.61 | 0.37 |
| 1 | 1 | 864 | 5 | 290 | 0.04 | -0.39 | -0.78 | -0.65 | -0.72 | -0.61 | 0.47 |
| 1 | 1 | 865 | 4 | 606 | 0.07 | 0.34 | 0.92 | -0.55 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 866 | 2 | 341 | 0.02 | -0.63 | 0.92 | -1.08 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 867 | 6 | 638 | 0.07 | 0.28 | 0.09 | 0.32 | 0.06 | 0.55 | 0.34 |
| 1 | 1 | 868 | 6 | 382 | 0.05 | -0.46 | 0.92 | -0.72 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 869 | 2 | 108 | 0.01 | -0.98 | -0.78 | -1.12 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 870 | 5 | 481 | 0.05 | -0.44 | -0.78 | -0.08 | 0.06 | 0.55 | 0.17 |
| 1 | 1 | 871 | 4 | 884 | 0.04 | 1.09 | 1.38 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 872 | 3 | 111 | 0.02 | -1.17 | 0.09 | -1.18 | -1.11 | -0.61 | 1.01 |
| 1 | 1 | 873 | 6 | 699 | 0.08 | 0.65 | 0.92 | 0.38 | 0.06 | 0.55 | 0.44 |
| 1 | 1 | 874 | 3 | 561 | 0.02 | -0.16 | 0.09 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 875 | 7 | 488 | 0.05 | -0.24 | -0.78 | 0.33 | 0.06 | -0.61 | -0.36 |
| 1 | 1 | 876 | 3 | 414 | 0.07 | -1.19 | 0.09 | -0.08 | 0.06 | -0.61 | -0.92 |
| 1 | 1 | 877 | 3 | 630 | 0.06 | 0.24 | 0.92 | 0.20 | 0.06 | -0.61 | -0.39 |
| 1 | 1 | 878 | 4 | 18 | 0.04 | -1.60 | -1.20 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 879 | 4 | 585 | 0.03 | 0.29 | 0.09 | 0.33 | 0.06 | -0.61 | 0.53 |
| 1 | 1 | 880 | 5 | 450 | 0.04 | -0.06 | -1.20 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 881 | 3 | 705 | 0.01 | -0.04 | -0.78 | 0.82 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 882 | 10 | 539 | 0.14 | 0.12 | 1.01 | 0.16 | -0.72 | -0.61 | 0.57 |
| 1 | 1 | 883 | 3 | 497 | 0.06 | 0.74 | -0.78 | -0.53 | 0.06 | -0.61 | 0.30 |
| 1 | 1 | 884 | 6 | 96 | 0.12 | -1.75 | -0.78 | -0.61 | -0.78 | -0.61 | -0.77 |
| 1 | 1 | 885 | 8 | 973 | 0.23 | 2.86 | 0.92 | 0.53 | 1.63 | 2.88 | 0.55 |
| 1 | 1 | 886 | 5 | 255 | 0.09 | -0.51 | -0.87 | -0.68 | -0.72 | 0.55 | -1.08 |
| 1 | 1 | 887 | 6 | 314 | 0.04 | -0.12 | -0.78 | -0.68 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 888 | 9 | 269 | 0.03 | -0.57 | -0.78 | -0.68 | -0.72 | -0.61 | 0.40 |
| 1 | 1 | 889 | 5 | 71 | 0.04 | -1.31 | -1.20 | -1.18 | -1.11 | -0.61 | 0.09 |
| 1 | 1 | 890 | 9 | 880 | 0.08 | 1.14 | 0.92 | 0.84 | 1.63 | 0.55 | 0.47 |
| 1 | 1 | 891 | 4 | 399 | 0.03 | -0.76 | 0.09 | 0.31 | -0.72 | -0.61 | 0.40 |
| 1 | 1 | 892 | 2 | 994 | 0.08 | 0.16 | -0.35 | 3.06 | 3.19 | 2.88 | -1.30 |
| 1 | 1 | 893 | 3 | 769 | 0.07 | 0.14 | -0.78 | 0.86 | 0.06 | 2.88 | 0.33 |
| 1 | 1 | 894 | 4 | 142 | 0.02 | -0.68 | -0.78 | -1.18 | -1.11 | -0.61 | 0.87 |
| 1 | 1 | 895 | 2 | 242 | 0 | -0.80 | -0.78 | -0.68 | -0.72 | -0.61 | 0.04 |
| 1 | 1 | 896 | 3 | 454 | 0.07 | -0.64 | 0.09 | -0.08 | -0.72 | 2.88 | -0.51 |
| 1 | 1 | 897 | 12 | 175 | 0.06 | -1.05 | -1.20 | -0.72 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 898 | 3 | 536 | 0.08 | -0.69 | 0.92 | -0.08 | 0.06 | -0.61 | 1.35 |
| 1 | 1 | 899 | 4 | 33 | 0.08 | -1.69 | -1.20 | -1.04 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 900 | 5 | 332 | 0.08 | -0.61 | 0.92 | -1.18 | -1.11 | 0.55 | 0.63 |
| 1 | 1 | 901 | 4 | 149 | 0.04 | -1.39 | -1.20 | -0.61 | -0.72 | -0.61 | 0.36 |
| 1 | 1 | 902 | 3 | 625 | 0.04 | 0.60 | 0.09 | 0.03 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 903 | 8 | 512 | 0.06 | -0.50 | -0.78 | 0.82 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 904 | 2 | 601 | 0.01 | 0.17 | 0.09 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 905 | 2 | 174 | 0.05 | -1.10 | -0.99 | -1.18 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 906 | 7 | 641 | 0.05 | 0.34 | 0.92 | 0.33 | 0.06 | -0.61 | 0.39 |
| 1 | 1 | 907 | 1 | 432 | 0 | 0.21 | -0.78 | -0.53 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 908 | 7 | 891 | 0.1 | 1.20 | 0.92 | 0.77 | 1.63 | 0.55 | -0.31 |
| 1 | 1 | 909 | 2 | 513 | 0.01 | -0.88 | 0.92 | -0.08 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 910 | 5 | 869 | 0.08 | 1.48 | 0.92 | -0.07 | 1.63 | 0.55 | 0.73 |
| 1 | 1 | 911 | 3 | 856 | 0.01 | 1.60 | 0.92 | -0.08 | 0.06 | 0.55 | -2.35 |
| 1 | 1 | 912 | 5 | 528 | 0.03 | -0.24 | -0.78 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 913 | 5 | 411 | 0.04 | 0.36 | 0.09 | -0.65 | -0.72 | -0.61 | 0.11 |
| 1 | 1 | 914 | 4 | 986 | 0.04 | 1.74 | -0.78 | 3.06 | 3.19 | 0.55 | -0.62 |
| 1 | 1 | 915 | 2 | 941 | 0.01 | 1.55 | 2.67 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 916 | 3 | 831 | 0.08 | 1.20 | 0.92 | -0.75 | -0.72 | 2.88 | 0.54 |
| 1 | 1 | 917 | 4 | 82 | 0.03 | -1.37 | -1.20 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 918 | 3 | 500 | 0.05 | -0.02 | 0.92 | -0.08 | -0.72 | -0.61 | -0.40 |
| 1 | 1 | 919 | 3 | 248 | 0.02 | 0.05 | -1.20 | -1.12 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 920 | 1 | 980 | 0 | 1.55 | 2.67 | 3.06 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 921 | 5 | 28 | 0.09 | -0.71 | -0.78 | 0.00 | -0.72 | 2.88 | -0.66 |
| 1 | 1 | 922 | 13 | 11 | 0.08 | -1.30 | -1.20 | -0.94 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 923 | 5 | 711 | 0.06 | 0.52 | 0.92 | -0.50 | 0.06 | 0.55 | -2.26 |
| 1 | 1 | 924 | 4 | 708 | 0.13 | 1.57 | 0.92 | -0.05 | -0.72 | 0.55 | -0.28 |
| 1 | 1 | 925 | 4 | 469 | 0.07 | -0.57 | 0.92 | 0.34 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 926 | 6 | 404 | 0.07 | -0.92 | 0.92 | -0.08 | -0.72 | -0.61 | 0.27 |
| 1 | 1 | 927 | 8 | 270 | 0.04 | -0.62 | -0.78 | -0.62 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 928 | 5 | 687 | 0.11 | 0.47 | 0.92 | 0.16 | 0.06 | 0.55 | -0.41 |
| 1 | 1 | 929 | 6 | 475 | 0.02 | -0.35 | -0.78 | 0.33 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 930 | 3 | 535 | 0.07 | -0.46 | 0.92 | -0.23 | 0.06 | -0.61 | -0.06 |
| 1 | 1 | 931 | 4 | 307 | 0.07 | -1.53 | 0.09 | -0.08 | -0.72 | -0.61 | -0.19 |
| 1 | 1 | 932 | 5 | 533 | 0.07 | -0.34 | -0.78 | 0.84 | 0.06 | -0.61 | 1.32 |
| 1 | 1 | 933 | 3 | 16 | 0.1 | -1.41 | -1.06 | -1.16 | -0.98 | 0.55 | 1.52 |
| 1 | 1 | 934 | 2 | 795 | 0.04 | 2.18 | 0.92 | -0.02 | 0.06 | 0.55 | -0.11 |
| 1 | 1 | 935 | 3 | 132 | 0.02 | -1.17 | 0.09 | -1.18 | -1.11 | -0.61 | 0.77 |
| 1 | 1 | 936 | 5 | 197 | 0.04 | -1.29 | -0.78 | -0.65 | -0.72 | -0.61 | 0.30 |
| 1 | 1 | 937 | 4 | 425 | 0.04 | 0.08 | 2.67 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 938 | 3 | 775 | 0.06 | 1.11 | 0.92 | 0.82 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 939 | 4 | 522 | 0.06 | -0.27 | -1.20 | 0.82 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 940 | 3 | 732 | 0.09 | 1.50 | 0.92 | -0.55 | 0.06 | -0.61 | -2.15 |
| 1 | 1 | 941 | 4 | 520 | 0.1 | 0.03 | 0.92 | -0.69 | -0.72 | 0.55 | -1.23 |
| 1 | 1 | 942 | 12 | 81 | 0.07 | -0.46 | 0.09 | -0.96 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 943 | 9 | 664 | 0.04 | 0.68 | 0.92 | 0.33 | 0.06 | -0.61 | 0.81 |
| 1 | 1 | 944 | 4 | 829 | 0.06 | 1.22 | 0.09 | 0.82 | 1.63 | -0.61 | -0.44 |
| 1 | 1 | 945 | 2 | 716 | 0.01 | 0.12 | -1.20 | 0.82 | 1.63 | -0.61 | 0.37 |
| 1 | 1 | 946 | 3 | 923 | 0.13 | 2.37 | 0.92 | -0.30 | -0.72 | 2.88 | 0.08 |
| 1 | 1 | 947 | 4 | 36 | 0.01 | -0.97 | -0.78 | -0.89 | -0.72 | -0.61 | -2.36 |
| 1 | 1 | 948 | 5 | 811 | 0.03 | 1.03 | 0.09 | 0.83 | 1.63 | -0.61 | 0.76 |
| 1 | 1 | 949 | 7 | 336 | 0.06 | -0.29 | -0.78 | -0.65 | -0.72 | 0.55 | 0.18 |
| 1 | 1 | 950 | 5 | 628 | 0.02 | -0.57 | 0.92 | 0.84 | 0.06 | -0.61 | -0.84 |
| 1 | 1 | 951 | 5 | 171 | 0.03 | -0.83 | 0.09 | -1.18 | -1.11 | -0.61 | 0.89 |
| 1 | 1 | 952 | 5 | 67 | 0.06 | -1.21 | -1.20 | -0.98 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 953 | 9 | 484 | 0.03 | -0.31 | -0.78 | 0.33 | 0.06 | -0.61 | 0.43 |
| 1 | 1 | 954 | 1 | 158 | 0 | 0.89 | -0.78 | -0.60 | -0.72 | -0.61 | -2.37 |
| 1 | 1 | 955 | 4 | 542 | 0.1 | 0.12 | -0.89 | -0.08 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 956 | 6 | 375 | 0.09 | -0.51 | 0.92 | -0.59 | -0.72 | -0.61 | -1.00 |
| 1 | 1 | 957 | 8 | 118 | 0.03 | -0.99 | -0.78 | -1.18 | -1.11 | -0.61 | 0.88 |
| 1 | 1 | 958 | 6 | 271 | 0.1 | -0.66 | -0.92 | -0.65 | -0.72 | 0.55 | -0.45 |
| 1 | 1 | 959 | 3 | 858 | 0.02 | 0.68 | 0.92 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 960 | 3 | 221 | 0.03 | -1.65 | -0.78 | -0.08 | -0.72 | -0.61 | 0.47 |
| 1 | 1 | 961 | 5 | 545 | 0.08 | 0.03 | 0.09 | -0.62 | 0.06 | 0.55 | 0.30 |
| 1 | 1 | 962 | 4 | 423 | 0.07 | -0.40 | -0.78 | -0.53 | 0.06 | 0.55 | 0.77 |
| 1 | 1 | 963 | 4 | 749 | 0.13 | 0.17 | 0.09 | -0.08 | 0.06 | 2.88 | -0.29 |
| 1 | 1 | 964 | 14 | 919 | 0.19 | 2.87 | 0.68 | 0.55 | 0.06 | -0.61 | -2.32 |
| 1 | 1 | 965 | 2 | 698 | 0.03 | 0.46 | 0.92 | 0.82 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 966 | 10 | 694 | 0.14 | 0.29 | -0.78 | -0.14 | 0.06 | 2.88 | 0.13 |
| 1 | 1 | 967 | 2 | 853 | 0.04 | 0.92 | 0.92 | -0.69 | -0.72 | 2.88 | 1.27 |
| 1 | 1 | 968 | 6 | 491 | 0.16 | 1.03 | 0.09 | -0.47 | -0.72 | -0.61 | 0.45 |
| 1 | 1 | 969 | 5 | 692 | 0.12 | 0.18 | 1.01 | 0.17 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 970 | 6 | 110 | 0.07 | -0.63 | -0.78 | -0.89 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 971 | 5 | 946 | 0.04 | 0.98 | 2.67 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 972 | 4 | 398 | 0.03 | -0.23 | 0.92 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 973 | 6 | 748 | 0.09 | -0.22 | -0.78 | 3.06 | 0.06 | -0.61 | -0.92 |
| 1 | 1 | 974 | 6 | 943 | 0.18 | 2.61 | 0.92 | 0.33 | 0.06 | 2.88 | 0.11 |
| 1 | 1 | 975 | 4 | 678 | 0.09 | 2.02 | -0.78 | 0.46 | 0.06 | -0.61 | 0.52 |
| 1 | 1 | 976 | 7 | 430 | 0.06 | -0.01 | 0.92 | -0.63 | -0.72 | -0.61 | 0.09 |
| 1 | 1 | 977 | 4 | 480 | 0.13 | -0.53 | 0.09 | -0.18 | -0.72 | 2.88 | 0.27 |
| 1 | 1 | 978 | 4 | 373 | 0.1 | -0.71 | 0.09 | 0.11 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 979 | 6 | 212 | 0.06 | -1.23 | -0.78 | -0.69 | -0.72 | 0.55 | 0.07 |
| 1 | 1 | 980 | 8 | 339 | 0.06 | -0.05 | -0.78 | -0.56 | 0.06 | -0.61 | -2.30 |
| 1 | 1 | 981 | 3 | 736 | 0.02 | 0.35 | -0.78 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 982 | 10 | 357 | 0.11 | -1.15 | -0.95 | -0.08 | 0.06 | -0.61 | 0.54 |
| 1 | 1 | 983 | 4 | 764 | 0.14 | 0.12 | -0.78 | 0.36 | 0.06 | 2.88 | 1.39 |
| 1 | 1 | 984 | 6 | 758 | 0.03 | 0.74 | -0.78 | 0.84 | 1.63 | -0.61 | 0.78 |
| 1 | 1 | 985 | 7 | 824 | 0.05 | 1.33 | 0.09 | 0.82 | 1.63 | -0.61 | 0.57 |
| 1 | 1 | 986 | 4 | 208 | 0.02 | -1.27 | 0.09 | -1.08 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 987 | 11 | 313 | 0.03 | -0.14 | -0.78 | -0.68 | -0.72 | -0.61 | 0.14 |
| 1 | 1 | 988 | 4 | 677 | 0.14 | -0.34 | 0.92 | -0.29 | -0.72 | 2.88 | 0.08 |
| 1 | 1 | 989 | 8 | 729 | 0.18 | 0.16 | 2.67 | 0.27 | 0.06 | -0.61 | -0.43 |
| 1 | 1 | 990 | 6 | 263 | 0.05 | -0.93 | -0.78 | -0.63 | -0.72 | 0.55 | 0.39 |
| 1 | 1 | 991 | 2 | 148 | 0.04 | -1.24 | -0.78 | -1.15 | -0.72 | -0.61 | 0.37 |
| 1 | 1 | 992 | 4 | 490 | 0.06 | -0.47 | 0.92 | 0.31 | -0.72 | -0.61 | -0.24 |
| 1 | 1 | 993 | 5 | 629 | 0.05 | -0.41 | 0.92 | 0.81 | 0.06 | -0.61 | -0.49 |
| 1 | 1 | 994 | 6 | 12 | 0.17 | -0.39 | 2.67 | -1.02 | -0.98 | -0.61 | 1.52 |
| 1 | 1 | 995 | 20 | 492 | 0.09 | 0.17 | 0.09 | -0.56 | 0.06 | -0.61 | -2.29 |
| 1 | 1 | 996 | 8 | 931 | 0.11 | 1.25 | 0.92 | -0.53 | 0.06 | 2.88 | -2.13 |
| 1 | 1 | 997 | 6 | 590 | 0.06 | 0.70 | 0.09 | -0.08 | 0.06 | -0.61 | 0.23 |
| 1 | 1 | 998 | 3 | 478 | 0.03 | -0.35 | -0.78 | 0.33 | 0.06 | -0.61 | 0.18 |
| 1 | 1 | 999 | 3 | 762 | 0.07 | 1.05 | 0.92 | -0.70 | 0.06 | 0.55 | -2.28 |
| 1 | 1 | 1000 | 4 | 683 | 0.06 | -0.60 | 0.92 | 0.87 | 0.06 | 0.55 | -0.78 |
| 1 | 1 | 1001 | 4 | 47 | 0.02 | -0.82 | -0.78 | -0.89 | -0.72 | -0.61 | -2.25 |
Now let us understand what each column in the above summary table means:
Segment.Level - Layers of the cell.
In this case, we have performed Vector Quantization for depth 1. Hence
Segment Level is 1
Segment.Parent - Parent segment of
the cell
Segment.Child (Cell.Number) - The
children of a particular cell. In this case, it is the total number of
cells at which we achieved the defined compression percentage
n - No of points in each
cell
Cell.ID - Cell_ID’s are generated
for the multivariate data using 1-D Sammon’s Projection
algorithm
Quant.Error - Quantization Error
for each cell
All the columns after this will contain centroids for each cell. They can also be called a codebook, which represents a collection of all centroids or codewords.
Step 2: Data Projection
lets view the projected 2D centroids after performing sammon’s projection on the compressed data recieved after performing vector quantization. For the shake of brevity we are displaying first six rows.
hvt_torus_coordinates <-map_A[[2]][[1]][["1"]]
centroids <<- list()
coordinates_value <- lapply(1:length(hvt_torus_coordinates), function(x){
centroids <-hvt_torus_coordinates[[x]]
coordinates <- centroids$pt
})
centroid_coordinates<<- do.call(rbind.data.frame, coordinates_value)
colnames(centroid_coordinates) <- c("x","y")
centroid_coordinates <- centroid_coordinates %>% data.frame() %>% round(4)
Table(head(centroid_coordinates))| x | y |
|---|---|
| 10.1594 | -3.0833 |
| 9.0798 | -11.7843 |
| 7.6534 | -3.3278 |
| -5.6131 | -6.2331 |
| -0.5750 | -8.9096 |
| -4.6926 | -24.8100 |
Step 3: Tessellation
Now, we have obtained the centroid coordinates resulting from the application of Sammon’s projection.
For better visualisation, let’s plot the Voronoi tessellation for Map
A using the plotHVT function.
# Voronoi tessellation plot for level one
muHVT::plotHVT(map_A,
line.width = c(0.2),
color.vec = c("#141B41"),
centroid.size = 0.01, #1.5
maxDepth = 1)Figure 3: The Voronoi Tessellation for layer 1 (map A) shown for the 1001 cells in the dataset ’computers’
Now let’s plot the Voronoi Tessellation with the heatmap overlaid for all the features in the computers dataset for better visualization.
metric_list <- colnames(trainComputers)
hmap <- list()
hmap <- lapply(1:length(metric_list), function(x){
muHVT::hvtHmap(
map_A,
trainComputers,
child.level = 1,
hmap.cols = metric_list[[x]],
line.width = c(0.2),
color.vec = c("#141B41"),
palette.color = 6,
centroid.size = 0.01,
show.points = T,
quant.error.hmap = 0.1,
n_cells.hmap = 1001
)
})The heatmaps displayed below provides a visual representation of the spatial characteristics of the computers data, allowing us to observe patterns and trends in the distribution of each of the features (price,speed,hd,ram,screen,ads). The sheer green shades highlight regions with higher values in each of the heatmaps, while the indigo shades indicate areas with the lowest values in each of the heatmaps. By analyzing these heatmaps, we can gain insights into the variations and relationships between each of these features within the computers data
grid.arrange(hmap[[1]], nrow = 1, ncol=1)grid.arrange(hmap[[2]], nrow = 1, ncol=1)grid.arrange(hmap[[3]], nrow = 1, ncol=1)grid.arrange(hmap[[4]], nrow = 1, ncol=1)grid.arrange(hmap[[5]], nrow = 1, ncol=1)grid.arrange(hmap[[6]], nrow = 1, ncol=1)In this section, we will manually figure out the novelty cells from the plotted map A and store it in identified_Novelty_cells variable.
The identified_Novelty_cells along with the map A is passed to removeNovelty() function.
The output of removeNovelty() function is a list of two items: a dataset with novelty records and the subset of the dataset without novelty records.
library(plyr)
identified_Novelty_cells <<- c(213,384)
output_list <- removeNovelty(identified_Novelty_cells, map_A)
dataset_with_novelty <- output_list[[1]]
dataset_without_novelty <- output_list[[2]]The datatable displayed below are the data with novelties. For the shake of brevity we are displaying first nine rows.
colnames(dataset_with_novelty) <- c("Cell.ID","Segment.Child","price","speed","hd","ram","screen","ads")
dataset_with_novelty%>% head(100) %>% as.data.frame() %>%
Table(scroll = T, limit = 20)| Cell.ID | Segment.Child | price | speed | hd | ram | screen | ads |
|---|---|---|---|---|---|---|---|
| 188 | 213 | -1.0521541 | -0.7832055 | -0.6759793 | -0.718149 | -0.6148117 | -0.2979247 |
| 188 | 213 | -1.1295176 | -0.7832055 | -0.6950076 | -0.718149 | -0.6148117 | -0.2979247 |
| 188 | 213 | -0.9529271 | -0.7832055 | -0.6759793 | -0.718149 | -0.6148117 | -0.6169727 |
| 188 | 213 | -1.1295176 | -0.7832055 | -0.6950076 | -0.718149 | -0.6148117 | -0.6169727 |
| 188 | 213 | -1.1446539 | -0.7832055 | -0.6759793 | -0.718149 | -0.6148117 | -0.6169727 |
| 188 | 213 | -1.0521541 | -0.7832055 | -0.6759793 | -0.718149 | -0.6148117 | -0.6169727 |
| 188 | 213 | -1.2051992 | -0.7832055 | -0.6759793 | -0.718149 | -0.6148117 | -0.6169727 |
| 1000 | 384 | 2.4796551 | 0.9160675 | 8.2958340 | 1.628450 | 0.5490304 | 0.4677905 |
| 1000 | 384 | 2.0423835 | 0.9160675 | 8.2958340 | 1.628450 | 0.5490304 | 0.0689805 |
The plotCells function is used to plot the Voronoi tessellation using the compressed HVT map (map A) and highlights the identified outlier cell(s) in red on the map.
Let’s look at the Voronoi tessellation with the novelty cell(s) in the map highlighted in red.
plotCells(identified_Novelty_cells, map_A, line.width = c(0.2),centroid.size = 0.01 )Figure 4: The Voronoi Tessellation with the novelty cells in the map highlighted in red
We pass the dataframe with novelty records to HVT function along with below mentioned model parameters to generate map B (layer 2).
Model Parameters
dataset_with_novelty <- dataset_with_novelty[,-1:-2]
map_B <- list()
map_B <- muHVT::HVT(dataset_with_novelty,
n_cells = 3,
depth = 1,
quant.err = 0.1,
projection.scale = 10,
normalize = F,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans",
diagnose = F)The datatable displayed below is the summary from map B.
summaryTable(map_B[[3]]$summary)| Segment.Level | Segment.Parent | Segment.Child | n | Cell.ID | Quant.Error | price | speed | hd | ram | screen | ads |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 2 | 2 | 0.01 | -1.09 | -0.78 | -0.69 | -0.72 | -0.61 | -0.30 |
| 1 | 1 | 2 | 2 | 1 | 0.07 | 2.26 | 0.92 | 8.30 | 1.63 | 0.55 | 0.27 |
| 1 | 1 | 3 | 5 | 3 | 0.02 | -1.10 | -0.78 | -0.68 | -0.72 | -0.61 | -0.62 |
Now let’s check the compression summary. The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.
compressionSummaryTable(map_B[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 3 | 3 | 1 | n_cells: 3 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
It can be observed from the table above that 3 cells out of 3
i.e. 100% of the cells has hit the
Quantization Error threshold
With the Novelties removed, we construct another hierarchical Voronoi tessellation map C layer 2 on the dataset without Novelty and below mentioned model parameters.
Model Parameters
map_C <- list()
map_C <- muHVT::HVT(dataset_without_novelty,
n_cells = 1001,
depth = 1,
quant.err = 0.1,
projection.scale = 10,
normalize = F,
distance_metric = "L1_Norm",
error_metric = "max",
quant_method = "kmeans",
diagnose = F)The datatable displayed below is the summary from map C
summaryTable(map_C[[3]]$summary)| Segment.Level | Segment.Parent | Segment.Child | n | Cell.ID | Quant.Error | price | speed | hd | ram | screen | ads |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 5 | 952 | 0.09 | 0.79 | -0.78 | 1.73 | 1.63 | 2.88 | -0.94 |
| 1 | 1 | 2 | 6 | 187 | 0.04 | -1.53 | -0.78 | -0.08 | -0.72 | -0.61 | -0.69 |
| 1 | 1 | 3 | 9 | 655 | 0.1 | 0.14 | 0.92 | 0.06 | 0.06 | 0.55 | 0.86 |
| 1 | 1 | 4 | 2 | 589 | 0.01 | -0.20 | 0.92 | -0.50 | 0.06 | 0.55 | 0.08 |
| 1 | 1 | 5 | 1 | 24 | 0 | 0.18 | -1.20 | -0.69 | -0.72 | 2.88 | -0.30 |
| 1 | 1 | 6 | 7 | 719 | 0.06 | 0.02 | 0.92 | 0.85 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 7 | 4 | 897 | 0.06 | 1.58 | 0.92 | 0.83 | 1.63 | 0.55 | 0.14 |
| 1 | 1 | 8 | 3 | 972 | 0.01 | 1.47 | 0.92 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 9 | 2 | 384 | 0.02 | -0.99 | 0.92 | -0.08 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 10 | 7 | 929 | 0.15 | 1.05 | 2.67 | 0.82 | 1.63 | -0.61 | -0.49 |
| 1 | 1 | 11 | 5 | 81 | 0.07 | -1.31 | -1.20 | -0.98 | -0.72 | 0.55 | 0.78 |
| 1 | 1 | 12 | 6 | 89 | 0.05 | -1.20 | -1.20 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 13 | 5 | 292 | 0.07 | -0.55 | 0.09 | -0.73 | -0.72 | -0.61 | -1.12 |
| 1 | 1 | 14 | 5 | 987 | 0.03 | 1.44 | -0.78 | 3.06 | 3.19 | 0.55 | -1.30 |
| 1 | 1 | 15 | 6 | 580 | 0.05 | -0.03 | 0.09 | -0.08 | 0.06 | 0.55 | 0.65 |
| 1 | 1 | 16 | 8 | 648 | 0.1 | 0.50 | 0.09 | -0.45 | 0.06 | 0.55 | -1.67 |
| 1 | 1 | 17 | 2 | 39 | 0.04 | -2.00 | -0.78 | -1.21 | -1.11 | -0.61 | 0.82 |
| 1 | 1 | 18 | 7 | 434 | 0.06 | 0.11 | 0.92 | -0.64 | -0.72 | -0.61 | -0.47 |
| 1 | 1 | 19 | 7 | 561 | 0.06 | -0.51 | 0.92 | 0.26 | 0.06 | -0.61 | -0.30 |
| 1 | 1 | 20 | 5 | 97 | 0.07 | 0.32 | -0.78 | -0.81 | -0.72 | -0.61 | -2.32 |
| 1 | 1 | 21 | 7 | 693 | 0.06 | 0.07 | 0.92 | 0.87 | 0.06 | 0.55 | 0.52 |
| 1 | 1 | 22 | 2 | 676 | 0.06 | 0.63 | 0.09 | 0.19 | 0.06 | 0.55 | -1.08 |
| 1 | 1 | 23 | 7 | 815 | 0.09 | 0.86 | 0.09 | 0.09 | 0.06 | 2.88 | 0.36 |
| 1 | 1 | 24 | 7 | 705 | 0.07 | 1.14 | 0.92 | -0.21 | 0.06 | 0.55 | 0.83 |
| 1 | 1 | 25 | 2 | 128 | 0.03 | -1.72 | -0.78 | -0.69 | -0.72 | -0.61 | -0.37 |
| 1 | 1 | 26 | 3 | 358 | 0.05 | -0.46 | 0.92 | -0.95 | -0.72 | -0.61 | 0.79 |
| 1 | 1 | 27 | 3 | 237 | 0.01 | -0.89 | -0.78 | -0.68 | -0.72 | -0.61 | 0.48 |
| 1 | 1 | 28 | 3 | 184 | 0.01 | -1.30 | -0.78 | -0.69 | -0.72 | -0.61 | 0.80 |
| 1 | 1 | 29 | 5 | 107 | 0.04 | -0.26 | 0.92 | -0.89 | -0.72 | -0.61 | -2.27 |
| 1 | 1 | 30 | 4 | 387 | 0.08 | -1.13 | 0.09 | -0.08 | -0.72 | 0.55 | 0.24 |
| 1 | 1 | 31 | 7 | 459 | 0.05 | -0.41 | -1.20 | 0.33 | 0.06 | -0.61 | 0.39 |
| 1 | 1 | 32 | 3 | 727 | 0.07 | 0.58 | 0.92 | 0.82 | 0.06 | 0.55 | -0.22 |
| 1 | 1 | 33 | 4 | 445 | 0.05 | -0.76 | -0.78 | -0.08 | 0.06 | 0.55 | 0.43 |
| 1 | 1 | 34 | 2 | 171 | 0.01 | -1.32 | -0.78 | -0.89 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 35 | 5 | 8 | 0.1 | -0.55 | -1.20 | -0.94 | -0.72 | 2.88 | 0.63 |
| 1 | 1 | 36 | 4 | 19 | 0.09 | 0.15 | -0.89 | -1.12 | -0.72 | 2.88 | 0.66 |
| 1 | 1 | 37 | 4 | 355 | 0.03 | -0.68 | 0.92 | -0.68 | -0.72 | -0.61 | 0.97 |
| 1 | 1 | 38 | 6 | 308 | 0.04 | -0.65 | 0.09 | -0.78 | -0.72 | -0.61 | 0.78 |
| 1 | 1 | 39 | 4 | 963 | 0.16 | 2.70 | 2.67 | 1.77 | 0.06 | 0.55 | -0.54 |
| 1 | 1 | 40 | 2 | 51 | 0.04 | -1.14 | 0.09 | -1.18 | -1.11 | 0.55 | 1.27 |
| 1 | 1 | 41 | 7 | 822 | 0.05 | 0.76 | 2.67 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 42 | 9 | 421 | 0.07 | -0.46 | -0.78 | -0.58 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 43 | 2 | 949 | 0.09 | 1.01 | 2.67 | 1.75 | 1.63 | 0.55 | -0.39 |
| 1 | 1 | 44 | 3 | 271 | 0.05 | -1.03 | 0.09 | -0.82 | -0.72 | -0.61 | 0.05 |
| 1 | 1 | 45 | 4 | 778 | 0.04 | 0.58 | 0.09 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 46 | 9 | 193 | 0.07 | -0.83 | 0.92 | -1.18 | -1.11 | -0.61 | 0.91 |
| 1 | 1 | 47 | 8 | 170 | 0.08 | -1.27 | -0.78 | -0.65 | -0.72 | 0.55 | -0.59 |
| 1 | 1 | 48 | 7 | 501 | 0.04 | -0.09 | -0.78 | 0.33 | 0.06 | -0.61 | 0.83 |
| 1 | 1 | 49 | 4 | 761 | 0.02 | 1.17 | -0.78 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 50 | 2 | 367 | 0.06 | -1.47 | 0.92 | -0.08 | -0.72 | -0.61 | -0.27 |
| 1 | 1 | 51 | 4 | 878 | 0.22 | 2.15 | 0.92 | 0.80 | -0.13 | 0.55 | -1.23 |
| 1 | 1 | 52 | 2 | 285 | 0.01 | -0.97 | -1.20 | -0.68 | 0.06 | -0.61 | 0.16 |
| 1 | 1 | 53 | 10 | 869 | 0.06 | 1.34 | 0.92 | 0.40 | 1.63 | 0.55 | 0.61 |
| 1 | 1 | 54 | 4 | 702 | 0.05 | 0.02 | 0.92 | 0.82 | 0.06 | 0.55 | -0.73 |
| 1 | 1 | 55 | 2 | 603 | 0.05 | -0.11 | 0.09 | 0.82 | 0.06 | -0.61 | 1.27 |
| 1 | 1 | 56 | 4 | 598 | 0.06 | 0.37 | 0.09 | 0.33 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 57 | 1 | 792 | 0 | 0.38 | 0.09 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 58 | 4 | 763 | 0.05 | 1.18 | 0.92 | 0.83 | 0.06 | 0.55 | 0.30 |
| 1 | 1 | 59 | 1 | 646 | 0 | 1.23 | -0.78 | -0.08 | 0.06 | 0.55 | 0.77 |
| 1 | 1 | 60 | 7 | 378 | 0.08 | -0.49 | -0.78 | 0.38 | -0.72 | -0.61 | 0.48 |
| 1 | 1 | 61 | 5 | 990 | 0.06 | 1.52 | 0.92 | 3.06 | 3.19 | 0.55 | -1.30 |
| 1 | 1 | 62 | 3 | 789 | 0.07 | 0.55 | 0.92 | 0.19 | -0.72 | 2.88 | 0.46 |
| 1 | 1 | 63 | 2 | 910 | 0.01 | 1.34 | 1.38 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 64 | 7 | 441 | 0.11 | 0.12 | 0.09 | -0.08 | -0.72 | -0.61 | 0.61 |
| 1 | 1 | 65 | 3 | 889 | 0.13 | 1.04 | 0.92 | -0.36 | 0.06 | 2.88 | -0.87 |
| 1 | 1 | 66 | 2 | 714 | 0.01 | 0.46 | -1.20 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 67 | 6 | 991 | 0.07 | 2.13 | 0.92 | 3.06 | 3.19 | 0.55 | -0.62 |
| 1 | 1 | 68 | 8 | 837 | 0.2 | 0.88 | -0.89 | 0.33 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 69 | 5 | 597 | 0.07 | 0.84 | 0.92 | -0.60 | -0.72 | 0.55 | 0.37 |
| 1 | 1 | 70 | 2 | 315 | 0.06 | -1.21 | 0.09 | -0.08 | -0.72 | -0.61 | 1.27 |
| 1 | 1 | 71 | 6 | 500 | 0.04 | -0.17 | -0.78 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 72 | 4 | 494 | 0.08 | -0.40 | -0.78 | 0.03 | 0.06 | 0.55 | 0.94 |
| 1 | 1 | 73 | 9 | 182 | 0.06 | -1.48 | -0.78 | -0.64 | -0.72 | -0.61 | 0.49 |
| 1 | 1 | 74 | 5 | 934 | 0.14 | 1.67 | 2.67 | 1.77 | 0.06 | -0.61 | -0.60 |
| 1 | 1 | 75 | 6 | 640 | 0.06 | 0.09 | 0.92 | -0.08 | 0.06 | 0.55 | 0.28 |
| 1 | 1 | 76 | 5 | 343 | 0.04 | -0.63 | 0.92 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 77 | 2 | 892 | 0.01 | 0.17 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 78 | 5 | 752 | 0.04 | 0.14 | -1.20 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 79 | 2 | 374 | 0.01 | 0.22 | -0.78 | -0.69 | 0.06 | -0.61 | -2.23 |
| 1 | 1 | 80 | 1 | 247 | 0 | -0.23 | -0.78 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 81 | 6 | 585 | 0.06 | 0.70 | 0.09 | -0.08 | 0.06 | -0.61 | 0.23 |
| 1 | 1 | 82 | 2 | 557 | 0.02 | -0.99 | 0.09 | 0.33 | 0.06 | 0.55 | -0.84 |
| 1 | 1 | 83 | 12 | 613 | 0.09 | -0.52 | 0.92 | 0.56 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 84 | 6 | 491 | 0.06 | -0.79 | -0.78 | 0.33 | 0.06 | 0.55 | 0.44 |
| 1 | 1 | 85 | 2 | 887 | 0.09 | 2.92 | 0.92 | 0.81 | -0.33 | 0.55 | 0.04 |
| 1 | 1 | 86 | 9 | 207 | 0.04 | -1.15 | -0.78 | -0.73 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 87 | 4 | 552 | 0.07 | -0.20 | -0.89 | 0.33 | 0.06 | 0.55 | -0.41 |
| 1 | 1 | 88 | 3 | 686 | 0.06 | 0.29 | 0.92 | 0.82 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 89 | 3 | 560 | 0.06 | 0.05 | 0.92 | -0.23 | 0.06 | -0.61 | 0.80 |
| 1 | 1 | 90 | 8 | 279 | 0.03 | -0.41 | -0.78 | -0.67 | -0.72 | -0.61 | 0.86 |
| 1 | 1 | 91 | 4 | 513 | 0.09 | -0.39 | -0.99 | 0.82 | 0.06 | -0.61 | 0.95 |
| 1 | 1 | 92 | 4 | 798 | 0.1 | 0.20 | -0.99 | 0.82 | 1.63 | 0.55 | 1.27 |
| 1 | 1 | 93 | 5 | 636 | 0.04 | 0.36 | 0.92 | 0.33 | 0.06 | -0.61 | 0.81 |
| 1 | 1 | 94 | 3 | 385 | 0.09 | 0.21 | -0.78 | -0.55 | -0.72 | 0.55 | -1.15 |
| 1 | 1 | 95 | 2 | 844 | 0.02 | 0.24 | -0.78 | 1.73 | 1.63 | 0.55 | 0.07 |
| 1 | 1 | 96 | 6 | 782 | 0.04 | 1.23 | -0.78 | 0.82 | 1.63 | -0.61 | 0.56 |
| 1 | 1 | 97 | 4 | 40 | 0.05 | -1.71 | -1.20 | -1.18 | -1.11 | -0.61 | 0.83 |
| 1 | 1 | 98 | 4 | 532 | 0.09 | 0.38 | 0.09 | -0.21 | -0.72 | 0.55 | 0.70 |
| 1 | 1 | 99 | 5 | 565 | 0.06 | 0.18 | -0.78 | 0.85 | 0.06 | -0.61 | 0.18 |
| 1 | 1 | 100 | 6 | 859 | 0.04 | 1.41 | 0.92 | 0.82 | 1.63 | -0.61 | 0.52 |
| 1 | 1 | 101 | 2 | 309 | 0.01 | -1.57 | 0.09 | -0.08 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 102 | 4 | 199 | 0.04 | -0.42 | -1.20 | -1.12 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 103 | 3 | 224 | 0.01 | -0.84 | -0.78 | -0.89 | -0.72 | -0.61 | 0.05 |
| 1 | 1 | 104 | 3 | 590 | 0.06 | 0.31 | -0.78 | 0.82 | 0.06 | -0.61 | -0.56 |
| 1 | 1 | 105 | 9 | 757 | 0.11 | 0.17 | 2.67 | 0.26 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 106 | 3 | 746 | 0.03 | 0.57 | -0.78 | 0.84 | 1.63 | -0.61 | 0.88 |
| 1 | 1 | 107 | 6 | 222 | 0.09 | -1.41 | 0.09 | -0.65 | -0.72 | -0.61 | -0.56 |
| 1 | 1 | 108 | 10 | 607 | 0.06 | 0.54 | 0.09 | 0.33 | 0.06 | -0.61 | 0.70 |
| 1 | 1 | 109 | 5 | 967 | 0.03 | 1.35 | -0.78 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 110 | 17 | 794 | 0.16 | 0.60 | -0.90 | 0.83 | 1.63 | 0.55 | 0.61 |
| 1 | 1 | 111 | 7 | 231 | 0.02 | -0.96 | -0.78 | -0.68 | -0.72 | -0.61 | 0.41 |
| 1 | 1 | 112 | 6 | 925 | 0.06 | 1.06 | 2.67 | 0.82 | 1.63 | -0.61 | 0.40 |
| 1 | 1 | 113 | 8 | 896 | 0.11 | 0.97 | 0.97 | 0.69 | 1.63 | 0.55 | 1.52 |
| 1 | 1 | 114 | 2 | 903 | 0 | 0.46 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 115 | 8 | 519 | 0.06 | -0.50 | -0.78 | 0.82 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 116 | 4 | 908 | 0.04 | 1.79 | 0.92 | 0.83 | 1.63 | 0.55 | 0.69 |
| 1 | 1 | 117 | 6 | 626 | 0.16 | 0.22 | -0.78 | 0.89 | -0.07 | 0.55 | 0.48 |
| 1 | 1 | 118 | 3 | 116 | 0.04 | -1.71 | -1.20 | -0.68 | -0.72 | -0.61 | 0.19 |
| 1 | 1 | 119 | 7 | 780 | 0.11 | 0.03 | 0.92 | 0.10 | 0.06 | 2.88 | 0.43 |
| 1 | 1 | 120 | 5 | 369 | 0.09 | 0.04 | 0.09 | -0.67 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 121 | 7 | 458 | 0.04 | -0.38 | -1.20 | 0.33 | 0.06 | -0.61 | 0.79 |
| 1 | 1 | 122 | 2 | 883 | 0.05 | 1.21 | 0.09 | 0.46 | 1.63 | 0.55 | -1.37 |
| 1 | 1 | 123 | 3 | 867 | 0.06 | 1.13 | -0.78 | 0.89 | 0.06 | 2.88 | 0.46 |
| 1 | 1 | 124 | 4 | 489 | 0.06 | -0.13 | -1.20 | 0.33 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 125 | 3 | 86 | 0.01 | -0.46 | -0.78 | -0.50 | -0.72 | -0.61 | -2.35 |
| 1 | 1 | 126 | 4 | 66 | 0.06 | -0.89 | 0.09 | -1.18 | -1.11 | -0.61 | -1.37 |
| 1 | 1 | 127 | 8 | 175 | 0.05 | -0.74 | -1.20 | -1.09 | -0.72 | -0.61 | 0.55 |
| 1 | 1 | 128 | 3 | 430 | 0.02 | -0.66 | -1.20 | 0.33 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 129 | 11 | 142 | 0.04 | -1.11 | -0.78 | -0.67 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 130 | 7 | 741 | 0.04 | 0.63 | -0.78 | 0.82 | 1.63 | -0.61 | 0.46 |
| 1 | 1 | 131 | 4 | 851 | 0.11 | 1.17 | 0.09 | 0.73 | 1.63 | 0.55 | -0.02 |
| 1 | 1 | 132 | 5 | 240 | 0.12 | -1.24 | -0.78 | 0.44 | -0.72 | -0.61 | -1.12 |
| 1 | 1 | 133 | 1 | 652 | 0 | 0.45 | 2.67 | -0.41 | -0.72 | -0.61 | -0.84 |
| 1 | 1 | 134 | 1 | 138 | 0 | -0.82 | -1.20 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 135 | 8 | 410 | 0.11 | 0.06 | 0.92 | -0.72 | -0.72 | -0.61 | -1.23 |
| 1 | 1 | 136 | 6 | 649 | 0.05 | -0.16 | 0.92 | 0.32 | 0.06 | 0.55 | 0.42 |
| 1 | 1 | 137 | 9 | 425 | 0.06 | -0.42 | -0.78 | -0.08 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 138 | 2 | 811 | 0.01 | 0.58 | 0.09 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 139 | 4 | 183 | 0.02 | -0.49 | -0.78 | -1.18 | -1.11 | -0.61 | 0.07 |
| 1 | 1 | 140 | 6 | 15 | 0.07 | -0.97 | -0.99 | -0.89 | -0.72 | 0.55 | -2.35 |
| 1 | 1 | 141 | 3 | 748 | 0.02 | 0.60 | -1.20 | 0.82 | 1.63 | -0.61 | 0.58 |
| 1 | 1 | 142 | 5 | 558 | 0.1 | 0.07 | 0.92 | -0.39 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 143 | 1 | 809 | 0 | 1.55 | -0.78 | 0.82 | 1.63 | -0.61 | 0.24 |
| 1 | 1 | 144 | 4 | 350 | 0.05 | -0.30 | 0.09 | -0.68 | -0.72 | -0.61 | -0.49 |
| 1 | 1 | 145 | 8 | 764 | 0.03 | 0.91 | -0.78 | 0.82 | 1.63 | -0.61 | 0.58 |
| 1 | 1 | 146 | 2 | 114 | 0.02 | -0.81 | -1.20 | -1.18 | -1.11 | -0.61 | 0.37 |
| 1 | 1 | 147 | 8 | 202 | 0.03 | -0.92 | -1.20 | -0.68 | -0.72 | -0.61 | 0.29 |
| 1 | 1 | 148 | 5 | 751 | 0.12 | 0.30 | -0.78 | 0.64 | 0.06 | 2.88 | 0.35 |
| 1 | 1 | 149 | 4 | 753 | 0.04 | 0.65 | -0.78 | 0.82 | 1.63 | -0.61 | -0.41 |
| 1 | 1 | 150 | 7 | 625 | 0.08 | 0.03 | 0.92 | 0.40 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 151 | 2 | 788 | 0.04 | 2.18 | 0.92 | -0.02 | 0.06 | 0.55 | -0.11 |
| 1 | 1 | 152 | 2 | 876 | 0.04 | -0.01 | 0.92 | 0.85 | 0.06 | 2.88 | -0.73 |
| 1 | 1 | 153 | 4 | 983 | 0.04 | 1.74 | -0.78 | 3.06 | 3.19 | 0.55 | -0.62 |
| 1 | 1 | 154 | 1 | 988 | 0 | 2.90 | 0.92 | 0.32 | 4.76 | 0.55 | 0.50 |
| 1 | 1 | 155 | 9 | 353 | 0.13 | -0.80 | -0.78 | -0.03 | -0.72 | 0.55 | 0.38 |
| 1 | 1 | 156 | 1 | 440 | 0 | -0.33 | 0.09 | -0.50 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 157 | 4 | 606 | 0.1 | -1.06 | 0.92 | 0.02 | 0.06 | 0.55 | -0.90 |
| 1 | 1 | 158 | 6 | 433 | 0.11 | -0.67 | 0.09 | 0.05 | -0.72 | 0.55 | 0.55 |
| 1 | 1 | 159 | 7 | 220 | 0.06 | -1.08 | -0.78 | -0.08 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 160 | 7 | 864 | 0.05 | 0.92 | 0.92 | 0.83 | 1.63 | 0.55 | 0.44 |
| 1 | 1 | 161 | 4 | 435 | 0.06 | 0.06 | 0.09 | -0.08 | -0.72 | -0.61 | 1.27 |
| 1 | 1 | 162 | 5 | 404 | 0.09 | 0.43 | -0.87 | -0.59 | 0.06 | -0.61 | -2.38 |
| 1 | 1 | 163 | 4 | 830 | 0.05 | 1.51 | 0.09 | 0.82 | 1.63 | -0.61 | 0.14 |
| 1 | 1 | 164 | 2 | 124 | 0.04 | -1.48 | -1.20 | -0.55 | -0.72 | -0.61 | -0.37 |
| 1 | 1 | 165 | 4 | 266 | 0.09 | -0.58 | 0.92 | -1.08 | -0.91 | -0.61 | -1.08 |
| 1 | 1 | 166 | 4 | 313 | 0.05 | -0.40 | -0.78 | -0.41 | -0.72 | -0.61 | -0.38 |
| 1 | 1 | 167 | 3 | 899 | 0.15 | 1.72 | -0.49 | -0.08 | 1.63 | -0.61 | -2.39 |
| 1 | 1 | 168 | 4 | 101 | 0.03 | -1.60 | -1.20 | -0.89 | -0.72 | -0.61 | 0.60 |
| 1 | 1 | 169 | 2 | 431 | 0.04 | -0.97 | 0.09 | -0.08 | 0.06 | -0.61 | 1.27 |
| 1 | 1 | 170 | 8 | 25 | 0.2 | -1.28 | -0.94 | -1.14 | -1.01 | 0.55 | -1.33 |
| 1 | 1 | 171 | 6 | 156 | 0.09 | -0.38 | -0.92 | -0.68 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 172 | 7 | 177 | 0.03 | -0.68 | -1.20 | -1.12 | -0.72 | -0.61 | 0.16 |
| 1 | 1 | 173 | 7 | 412 | 0.09 | -0.18 | 0.09 | -0.65 | -0.72 | 0.55 | 0.37 |
| 1 | 1 | 174 | 6 | 599 | 0.03 | -0.07 | 0.92 | 0.33 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 175 | 12 | 835 | 0.12 | 0.74 | 0.92 | -0.05 | 0.06 | 2.88 | 0.24 |
| 1 | 1 | 176 | 5 | 94 | 0.07 | -0.98 | -0.78 | -1.18 | -1.11 | 0.55 | 0.62 |
| 1 | 1 | 177 | 3 | 723 | 0.02 | 0.01 | -1.20 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 178 | 5 | 457 | 0.03 | -0.48 | -0.78 | 0.32 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 179 | 4 | 968 | 0.05 | 1.17 | -0.78 | 3.06 | 3.19 | 0.55 | 0.27 |
| 1 | 1 | 180 | 3 | 621 | 0.04 | 0.60 | 0.09 | 0.03 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 181 | 2 | 455 | 0.07 | -0.92 | 0.92 | -0.38 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 182 | 5 | 846 | 0.1 | 1.04 | 0.09 | 0.83 | 1.63 | 0.55 | 0.63 |
| 1 | 1 | 183 | 5 | 28 | 0.09 | -1.34 | -1.20 | -1.01 | -0.87 | -0.61 | -1.67 |
| 1 | 1 | 184 | 6 | 3 | 0.1 | -1.48 | -1.13 | -1.29 | -1.11 | -0.61 | -2.29 |
| 1 | 1 | 185 | 4 | 446 | 0.04 | 0.33 | 0.92 | -0.68 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 186 | 8 | 60 | 0.05 | -1.52 | -1.20 | -1.18 | -1.11 | -0.61 | 0.40 |
| 1 | 1 | 187 | 6 | 427 | 0.11 | 0.86 | -0.78 | -0.24 | -0.72 | -0.61 | 0.66 |
| 1 | 1 | 188 | 2 | 818 | 0.05 | 0.13 | 1.15 | 0.34 | 1.63 | 0.55 | -0.30 |
| 1 | 1 | 189 | 3 | 461 | 0.07 | -0.99 | 0.92 | -0.08 | -0.72 | 0.55 | 0.17 |
| 1 | 1 | 190 | 5 | 995 | 0.04 | 2.08 | 2.67 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 191 | 2 | 823 | 0.01 | 0.84 | 0.09 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 192 | 5 | 615 | 0.11 | 0.39 | 0.09 | -0.16 | 0.06 | 0.55 | 0.72 |
| 1 | 1 | 193 | 6 | 530 | 0.09 | 0.15 | 0.92 | -0.55 | -0.72 | 0.55 | 0.18 |
| 1 | 1 | 194 | 5 | 814 | 0.11 | 0.04 | 0.92 | 0.08 | 0.06 | 2.88 | -0.48 |
| 1 | 1 | 195 | 6 | 555 | 0.14 | 0.47 | 0.09 | -0.18 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 196 | 6 | 643 | 0.09 | 0.88 | 0.92 | -0.22 | 0.06 | -0.61 | 0.88 |
| 1 | 1 | 197 | 2 | 372 | 0.03 | -1.35 | 0.09 | -0.08 | -0.72 | 0.55 | -0.46 |
| 1 | 1 | 198 | 5 | 197 | 0.1 | -1.45 | -0.78 | -0.08 | -0.72 | -0.61 | 1.11 |
| 1 | 1 | 199 | 3 | 36 | 0.1 | -0.55 | -0.78 | 0.06 | -0.72 | 2.88 | -0.69 |
| 1 | 1 | 200 | 3 | 697 | 0.01 | -0.04 | -0.78 | 0.82 | 1.63 | -0.61 | 0.47 |
| 1 | 1 | 201 | 11 | 364 | 0.11 | -0.76 | 0.09 | -0.59 | -0.72 | 0.55 | 0.59 |
| 1 | 1 | 202 | 7 | 678 | 0.07 | 0.35 | 0.92 | 0.32 | 0.06 | 0.55 | 0.16 |
| 1 | 1 | 203 | 5 | 253 | 0.03 | -0.77 | -0.78 | -0.63 | -0.72 | -0.61 | 0.63 |
| 1 | 1 | 204 | 5 | 816 | 0.04 | 1.18 | 0.09 | 0.82 | 1.63 | -0.61 | 0.12 |
| 1 | 1 | 205 | 4 | 537 | 0.02 | 0.33 | -0.78 | 0.33 | 0.06 | -0.61 | 0.56 |
| 1 | 1 | 206 | 8 | 716 | 0.18 | 0.16 | 2.67 | 0.27 | 0.06 | -0.61 | -0.43 |
| 1 | 1 | 207 | 2 | 786 | 0.01 | 1.30 | 0.92 | 0.82 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 208 | 6 | 29 | 0.04 | -0.58 | -1.20 | -1.08 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 209 | 10 | 213 | 0.06 | -0.67 | -1.20 | -0.65 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 210 | 5 | 492 | 0.03 | -0.31 | -0.78 | -0.08 | 0.06 | 0.55 | 0.60 |
| 1 | 1 | 211 | 3 | 424 | 0.06 | -0.60 | 0.92 | -0.08 | -0.72 | -0.61 | -0.51 |
| 1 | 1 | 212 | 5 | 488 | 0.07 | 0.27 | -0.78 | -0.17 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 213 | 6 | 211 | 0.06 | -1.23 | -0.78 | -0.69 | -0.72 | 0.55 | 0.07 |
| 1 | 1 | 214 | 4 | 707 | 0.02 | 0.17 | -0.78 | 0.82 | 1.63 | -0.61 | 0.44 |
| 1 | 1 | 215 | 7 | 609 | 0.07 | 0.57 | 0.09 | 0.33 | 0.06 | -0.61 | 0.13 |
| 1 | 1 | 216 | 4 | 179 | 0.11 | -0.49 | -0.89 | -0.79 | -0.72 | 0.55 | 1.52 |
| 1 | 1 | 217 | 3 | 977 | 0.01 | 1.47 | 0.92 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 218 | 6 | 517 | 0.03 | 0.04 | -0.78 | 0.33 | 0.06 | -0.61 | 0.04 |
| 1 | 1 | 219 | 1 | 295 | 0 | -0.46 | -0.78 | -0.50 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 220 | 2 | 574 | 0.01 | -0.64 | -0.78 | 0.82 | 0.06 | 0.55 | -0.84 |
| 1 | 1 | 221 | 10 | 682 | 0.05 | 0.42 | 0.92 | 0.34 | 0.06 | 0.55 | 0.46 |
| 1 | 1 | 222 | 6 | 236 | 0.05 | -0.80 | -0.78 | -0.61 | -0.72 | -0.61 | -0.53 |
| 1 | 1 | 223 | 5 | 98 | 0.03 | -1.44 | -1.20 | -1.15 | -0.72 | -0.61 | 0.30 |
| 1 | 1 | 224 | 5 | 196 | 0.02 | -0.82 | -0.78 | -1.12 | -0.72 | -0.61 | 0.55 |
| 1 | 1 | 225 | 6 | 99 | 0.03 | -1.26 | -1.20 | -1.14 | -0.72 | -0.61 | 0.85 |
| 1 | 1 | 226 | 2 | 538 | 0.04 | 0.38 | 0.92 | -0.08 | -0.72 | -0.61 | 1.27 |
| 1 | 1 | 227 | 5 | 997 | 0.04 | 2.08 | 2.67 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 228 | 2 | 4 | 0.02 | -1.31 | -0.78 | -0.50 | -1.11 | 2.88 | 0.07 |
| 1 | 1 | 229 | 5 | 420 | 0.05 | -0.49 | -0.78 | -0.08 | 0.06 | -0.61 | 0.33 |
| 1 | 1 | 230 | 4 | 691 | 0.16 | -0.17 | -0.78 | 0.24 | 0.06 | 2.88 | -0.45 |
| 1 | 1 | 231 | 4 | 111 | 0.05 | -1.69 | -1.20 | -0.68 | -0.72 | -0.61 | 0.57 |
| 1 | 1 | 232 | 1 | 341 | 0 | 0.21 | -0.78 | -0.89 | 0.06 | -0.61 | -2.29 |
| 1 | 1 | 233 | 9 | 162 | 0.05 | 0.10 | 0.92 | -1.04 | -0.72 | -0.61 | -2.28 |
| 1 | 1 | 234 | 7 | 145 | 0.03 | -1.33 | -0.78 | -0.68 | -0.72 | -0.61 | -0.84 |
| 1 | 1 | 235 | 4 | 840 | 0.08 | 1.01 | 0.92 | -0.07 | 1.63 | 0.55 | -0.32 |
| 1 | 1 | 236 | 8 | 87 | 0.05 | -1.55 | -1.20 | -1.15 | -0.72 | -0.61 | 0.52 |
| 1 | 1 | 237 | 9 | 508 | 0.05 | -0.44 | -0.78 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 238 | 12 | 58 | 0.04 | -0.55 | -0.78 | -0.89 | -0.72 | -0.61 | -2.30 |
| 1 | 1 | 239 | 5 | 44 | 0.15 | -0.63 | -0.95 | -1.11 | 0.06 | -0.61 | -2.37 |
| 1 | 1 | 240 | 7 | 687 | 0.12 | 0.63 | 0.92 | -0.32 | 0.06 | 0.55 | -1.08 |
| 1 | 1 | 241 | 6 | 153 | 0.02 | -1.39 | -0.78 | -0.70 | -0.72 | -0.61 | -0.62 |
| 1 | 1 | 242 | 5 | 592 | 0.15 | 1.15 | 0.92 | -0.26 | -0.72 | -0.61 | -0.35 |
| 1 | 1 | 243 | 5 | 854 | 0.07 | 2.45 | 0.92 | 0.74 | 0.06 | 0.55 | 0.65 |
| 1 | 1 | 244 | 8 | 423 | 0.07 | -0.64 | 0.92 | -0.60 | -0.72 | 0.55 | 0.69 |
| 1 | 1 | 245 | 5 | 817 | 0.04 | 0.56 | 0.92 | 0.82 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 246 | 4 | 391 | 0.07 | -0.40 | 0.92 | -0.61 | -0.72 | -0.61 | -0.57 |
| 1 | 1 | 247 | 5 | 115 | 0.04 | -1.54 | -1.20 | -0.85 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 248 | 5 | 127 | 0.02 | -1.55 | -0.78 | -1.08 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 249 | 3 | 813 | 0.07 | 1.11 | 0.09 | -0.08 | -0.72 | 2.88 | 1.10 |
| 1 | 1 | 250 | 5 | 100 | 0.02 | -1.40 | -1.20 | -1.15 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 251 | 4 | 192 | 0.06 | -0.63 | 0.09 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 252 | 1 | 20 | 0 | -0.13 | 0.92 | -1.29 | -1.11 | -0.61 | -2.39 |
| 1 | 1 | 253 | 5 | 305 | 0.04 | -0.70 | 0.09 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 254 | 7 | 568 | 0.09 | -0.31 | 0.92 | 0.20 | 0.06 | -0.61 | 0.45 |
| 1 | 1 | 255 | 4 | 769 | 0.06 | 0.71 | -1.20 | 0.82 | 1.63 | -0.61 | -0.44 |
| 1 | 1 | 256 | 2 | 743 | 0.06 | 1.22 | 0.92 | 0.85 | 0.06 | -0.61 | -0.85 |
| 1 | 1 | 257 | 6 | 166 | 0.04 | -1.62 | -0.78 | -0.69 | -0.72 | -0.61 | 0.39 |
| 1 | 1 | 258 | 7 | 409 | 0.05 | -0.12 | 0.92 | -0.63 | -0.72 | -0.61 | 0.79 |
| 1 | 1 | 259 | 5 | 141 | 0.12 | -0.44 | 0.92 | -1.05 | -0.87 | -0.61 | -1.67 |
| 1 | 1 | 260 | 5 | 288 | 0.06 | -1.04 | 0.92 | -1.00 | -0.72 | -0.61 | 0.77 |
| 1 | 1 | 261 | 1 | 357 | 0 | -0.63 | -0.78 | 0.33 | -0.72 | -0.61 | -0.30 |
| 1 | 1 | 262 | 5 | 812 | 0.11 | 1.82 | 0.92 | 0.71 | 0.06 | 0.55 | 0.68 |
| 1 | 1 | 263 | 3 | 771 | 0.04 | 0.96 | -1.20 | 0.82 | 1.63 | -0.61 | 0.33 |
| 1 | 1 | 264 | 5 | 698 | 0.07 | -0.43 | 0.92 | 0.86 | 0.06 | 0.55 | -1.30 |
| 1 | 1 | 265 | 3 | 992 | 0.07 | 2.01 | 2.67 | 3.06 | 1.63 | 0.55 | 1.18 |
| 1 | 1 | 266 | 6 | 937 | 0.14 | 0.85 | 0.99 | 0.67 | 1.63 | 2.88 | 0.48 |
| 1 | 1 | 267 | 3 | 958 | 0.01 | 1.17 | -0.78 | 3.06 | 3.19 | -0.61 | 0.07 |
| 1 | 1 | 268 | 6 | 214 | 0.03 | -0.71 | -0.78 | -1.12 | -0.72 | -0.61 | 0.27 |
| 1 | 1 | 269 | 7 | 277 | 0.1 | -1.45 | -0.78 | -0.08 | -0.72 | 0.55 | 0.08 |
| 1 | 1 | 270 | 4 | 123 | 0.03 | -0.81 | -0.78 | -1.18 | -1.11 | 0.55 | 0.11 |
| 1 | 1 | 271 | 5 | 408 | 0.08 | -0.50 | 0.09 | -0.69 | 0.06 | -0.61 | 0.58 |
| 1 | 1 | 272 | 4 | 223 | 0.03 | -0.82 | -0.78 | -0.89 | -0.72 | -0.61 | 0.52 |
| 1 | 1 | 273 | 3 | 172 | 0.01 | -0.64 | -0.78 | -1.18 | -1.11 | -0.61 | 0.24 |
| 1 | 1 | 274 | 4 | 386 | 0.04 | -0.85 | 0.09 | 0.31 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 275 | 11 | 259 | 0.04 | -0.31 | -0.78 | -1.12 | -0.72 | -0.61 | 0.11 |
| 1 | 1 | 276 | 4 | 407 | 0.15 | 0.69 | -0.78 | -0.31 | -0.72 | -0.61 | -0.60 |
| 1 | 1 | 277 | 5 | 161 | 0.03 | -1.23 | -0.78 | -0.89 | -0.72 | -0.61 | 0.95 |
| 1 | 1 | 278 | 7 | 733 | 0.12 | 1.05 | 0.92 | 0.46 | 0.06 | 0.55 | 0.76 |
| 1 | 1 | 279 | 7 | 227 | 0.04 | -0.65 | -1.20 | -0.68 | -0.72 | -0.61 | 0.57 |
| 1 | 1 | 280 | 4 | 731 | 0.02 | 0.38 | -1.20 | 0.82 | 1.63 | -0.61 | 0.37 |
| 1 | 1 | 281 | 3 | 479 | 0.02 | -0.46 | 0.92 | 0.31 | -0.72 | -0.61 | -0.35 |
| 1 | 1 | 282 | 7 | 891 | 0.04 | 1.37 | 0.92 | 0.85 | 1.63 | 0.55 | 0.68 |
| 1 | 1 | 283 | 4 | 684 | 0.04 | 1.17 | 0.92 | 0.33 | 0.06 | -0.61 | 0.37 |
| 1 | 1 | 284 | 5 | 493 | 0.08 | 0.34 | -0.78 | -0.16 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 285 | 4 | 750 | 0.09 | 1.41 | 0.92 | 0.42 | 0.06 | 0.55 | 0.13 |
| 1 | 1 | 286 | 5 | 563 | 0.04 | 0.07 | 0.09 | 0.33 | 0.06 | -0.61 | 0.89 |
| 1 | 1 | 287 | 7 | 67 | 0.04 | -1.25 | -1.20 | -1.18 | -1.11 | -0.61 | 0.86 |
| 1 | 1 | 288 | 3 | 833 | 0.02 | 1.41 | 0.09 | 0.86 | 1.63 | -0.61 | 0.83 |
| 1 | 1 | 289 | 6 | 722 | 0.11 | 0.10 | 0.09 | -0.08 | 0.06 | 2.88 | 0.58 |
| 1 | 1 | 290 | 6 | 406 | 0.05 | -0.13 | 0.92 | -0.71 | -0.72 | -0.61 | 0.39 |
| 1 | 1 | 291 | 3 | 41 | 0.05 | -1.43 | -1.20 | -1.25 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 292 | 9 | 694 | 0.12 | 0.36 | -0.78 | -0.19 | 0.06 | 2.88 | 0.79 |
| 1 | 1 | 293 | 8 | 263 | 0.04 | -0.42 | -0.78 | -0.60 | 0.06 | -0.61 | -2.30 |
| 1 | 1 | 294 | 3 | 241 | 0.13 | -0.70 | 0.92 | -1.02 | -0.98 | 0.55 | -1.35 |
| 1 | 1 | 295 | 7 | 258 | 0.04 | -0.46 | -1.20 | -0.68 | -0.72 | -0.61 | 0.15 |
| 1 | 1 | 296 | 7 | 531 | 0.08 | -0.33 | -1.20 | 0.33 | 0.06 | 0.55 | 0.49 |
| 1 | 1 | 297 | 3 | 112 | 0.02 | -1.20 | -0.78 | -1.18 | -1.11 | -0.61 | 0.05 |
| 1 | 1 | 298 | 5 | 428 | 0.03 | 0.08 | 0.92 | -0.68 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 299 | 3 | 229 | 0.03 | -1.19 | 0.92 | -0.69 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 300 | 2 | 322 | 0.01 | -1.25 | 0.92 | -0.68 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 301 | 5 | 38 | 0.08 | -0.92 | -0.78 | -0.08 | -0.72 | 2.88 | 0.39 |
| 1 | 1 | 302 | 6 | 143 | 0.05 | -1.34 | -1.20 | -0.65 | -0.72 | -0.61 | 0.74 |
| 1 | 1 | 303 | 8 | 874 | 0.04 | 1.58 | 0.92 | 0.84 | 1.63 | -0.61 | 0.77 |
| 1 | 1 | 304 | 4 | 69 | 0.03 | -0.29 | 0.92 | -1.10 | -0.72 | -0.61 | -2.31 |
| 1 | 1 | 305 | 10 | 852 | 0.07 | 1.48 | 0.92 | 0.64 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 306 | 4 | 368 | 0.07 | -0.79 | 0.09 | -0.08 | -0.72 | -0.61 | 0.18 |
| 1 | 1 | 307 | 9 | 96 | 0.04 | -1.24 | -0.78 | -1.18 | -1.11 | -0.61 | 0.83 |
| 1 | 1 | 308 | 8 | 946 | 0.17 | 1.60 | 0.92 | 0.57 | 1.63 | 2.88 | 0.93 |
| 1 | 1 | 309 | 3 | 148 | 0.03 | -0.78 | -1.20 | -1.14 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 310 | 3 | 437 | 0.05 | -0.30 | 0.09 | 0.32 | -0.72 | -0.61 | 0.37 |
| 1 | 1 | 311 | 2 | 2 | 0.04 | -0.57 | -1.20 | -1.12 | -0.72 | 2.88 | 1.27 |
| 1 | 1 | 312 | 2 | 439 | 0.04 | -0.30 | 1.38 | -0.68 | -0.72 | 0.55 | 1.27 |
| 1 | 1 | 313 | 5 | 251 | 0.01 | -0.80 | -0.78 | -0.64 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 314 | 4 | 911 | 0.07 | 1.98 | 0.92 | 0.51 | 1.63 | 0.55 | 0.85 |
| 1 | 1 | 315 | 4 | 951 | 0.24 | 1.31 | 0.92 | 3.06 | 1.63 | 0.26 | -0.63 |
| 1 | 1 | 316 | 3 | 1001 | 0.04 | 5.26 | 0.92 | 4.01 | 4.76 | 2.88 | 0.46 |
| 1 | 1 | 317 | 4 | 311 | 0.03 | -0.49 | 0.92 | -1.18 | -1.11 | -0.61 | 0.05 |
| 1 | 1 | 318 | 8 | 221 | 0.05 | -0.76 | -1.20 | -0.68 | -0.72 | -0.61 | 0.10 |
| 1 | 1 | 319 | 3 | 436 | 0.06 | -0.28 | 0.09 | 0.32 | -0.72 | -0.61 | 0.85 |
| 1 | 1 | 320 | 9 | 880 | 0.05 | 0.45 | -0.78 | 1.73 | 1.63 | 0.55 | -0.67 |
| 1 | 1 | 321 | 5 | 354 | 0.04 | -0.23 | 0.09 | -0.68 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 322 | 5 | 329 | 0.06 | -0.63 | 0.09 | -0.65 | -0.72 | -0.61 | 0.45 |
| 1 | 1 | 323 | 5 | 790 | 0.04 | 1.13 | -0.78 | 0.82 | 1.63 | -0.61 | -0.44 |
| 1 | 1 | 324 | 2 | 832 | 0.07 | 0.22 | 1.15 | -0.08 | 1.63 | 0.55 | 1.52 |
| 1 | 1 | 325 | 6 | 429 | 0.03 | 0.13 | 0.92 | -0.68 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 326 | 5 | 151 | 0.04 | -1.18 | -1.20 | -0.85 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 327 | 5 | 944 | 0.16 | 2.59 | 0.92 | 0.64 | 0.06 | 2.88 | 1.14 |
| 1 | 1 | 328 | 5 | 805 | 0.08 | 1.22 | 0.92 | -0.08 | 0.06 | 0.55 | -2.18 |
| 1 | 1 | 329 | 5 | 767 | 0.04 | 0.48 | -0.78 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 330 | 4 | 711 | 0.1 | 1.04 | -0.78 | -0.08 | -0.72 | 2.88 | 0.92 |
| 1 | 1 | 331 | 1 | 203 | 0 | -0.30 | -0.78 | -1.18 | -1.11 | -0.61 | 0.24 |
| 1 | 1 | 332 | 6 | 204 | 0.06 | -1.23 | 0.09 | -1.01 | -0.72 | -0.61 | 0.75 |
| 1 | 1 | 333 | 11 | 765 | 0.41 | 0.03 | -0.78 | 3.16 | 0.06 | -0.40 | -0.36 |
| 1 | 1 | 334 | 6 | 139 | 0.02 | -0.96 | -0.78 | -1.18 | -1.11 | -0.61 | 0.05 |
| 1 | 1 | 335 | 4 | 210 | 0.04 | -0.77 | -1.20 | -0.68 | -0.72 | 0.55 | 0.82 |
| 1 | 1 | 336 | 2 | 600 | 0.02 | -0.51 | -0.78 | 0.82 | 0.06 | 0.55 | -1.30 |
| 1 | 1 | 337 | 4 | 831 | 0.04 | 0.75 | 0.92 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 338 | 5 | 706 | 0.1 | 1.21 | 0.92 | -0.18 | 0.06 | 0.55 | 0.26 |
| 1 | 1 | 339 | 4 | 680 | 0.06 | -0.60 | 0.92 | 0.87 | 0.06 | 0.55 | -0.78 |
| 1 | 1 | 340 | 3 | 250 | 0.01 | -0.82 | -0.78 | -0.65 | -0.72 | -0.61 | 0.24 |
| 1 | 1 | 341 | 9 | 886 | 0.1 | 0.53 | 2.67 | 0.75 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 342 | 4 | 349 | 0.06 | -0.99 | 0.92 | -0.56 | -0.72 | -0.61 | 0.43 |
| 1 | 1 | 343 | 4 | 121 | 0.08 | -1.35 | -1.20 | -0.75 | -0.72 | 0.55 | 0.18 |
| 1 | 1 | 344 | 2 | 838 | 0.04 | 0.54 | 0.09 | 0.82 | 1.63 | 0.55 | 1.27 |
| 1 | 1 | 345 | 5 | 167 | 0.04 | -0.96 | -0.78 | -1.14 | -0.72 | -0.61 | 0.89 |
| 1 | 1 | 346 | 6 | 882 | 0.17 | 0.41 | 0.92 | 1.02 | 0.06 | 2.88 | 0.14 |
| 1 | 1 | 347 | 8 | 576 | 0.06 | 0.19 | 0.92 | -0.57 | 0.06 | -0.61 | -2.28 |
| 1 | 1 | 348 | 8 | 63 | 0.1 | -0.76 | 1.15 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 349 | 5 | 302 | 0.03 | -1.17 | -0.78 | 0.33 | -0.72 | -0.61 | -0.33 |
| 1 | 1 | 350 | 2 | 527 | 0.08 | 0.37 | -0.35 | -0.08 | -0.72 | 0.55 | 1.52 |
| 1 | 1 | 351 | 3 | 536 | 0.06 | -0.30 | 0.09 | 0.33 | 0.06 | -0.61 | 0.93 |
| 1 | 1 | 352 | 8 | 774 | 0.11 | -0.22 | 0.92 | 1.77 | 0.06 | 0.55 | -0.96 |
| 1 | 1 | 353 | 6 | 474 | 0.05 | -0.23 | -1.20 | 0.33 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 354 | 3 | 375 | 0.05 | -0.62 | 0.92 | -0.50 | -0.72 | -0.61 | -0.84 |
| 1 | 1 | 355 | 4 | 969 | 0.24 | 3.11 | -0.56 | 3.30 | 1.63 | -0.61 | 0.68 |
| 1 | 1 | 356 | 5 | 146 | 0.03 | -0.99 | -0.78 | -0.68 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 357 | 1 | 898 | 0 | 0.54 | 0.92 | 1.73 | 1.63 | 0.55 | 0.07 |
| 1 | 1 | 358 | 3 | 572 | 0.05 | 1.21 | -0.78 | -0.08 | 0.06 | -0.61 | 0.50 |
| 1 | 1 | 359 | 1 | 909 | 0 | 0.71 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 360 | 10 | 642 | 0.08 | 0.07 | 0.92 | 0.38 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 361 | 3 | 396 | 0.02 | -0.66 | -0.78 | -0.56 | 0.06 | 0.55 | 0.87 |
| 1 | 1 | 362 | 5 | 215 | 0.02 | -0.79 | -0.78 | -0.89 | -0.72 | -0.61 | 0.88 |
| 1 | 1 | 363 | 3 | 742 | 0.1 | 1.90 | -0.20 | 0.45 | 0.06 | 0.55 | 0.05 |
| 1 | 1 | 364 | 2 | 246 | 0 | -0.80 | -0.78 | -0.68 | -0.72 | -0.61 | 0.04 |
| 1 | 1 | 365 | 6 | 274 | 0.03 | -0.47 | -0.78 | -0.69 | -0.72 | -0.61 | -0.42 |
| 1 | 1 | 366 | 4 | 704 | 0.09 | 0.66 | 0.92 | -0.40 | 0.06 | 0.55 | -1.67 |
| 1 | 1 | 367 | 4 | 306 | 0.05 | -0.16 | -0.78 | -0.65 | -0.72 | -0.61 | 0.90 |
| 1 | 1 | 368 | 2 | 49 | 0.02 | -1.35 | -1.20 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 369 | 7 | 842 | 0.07 | 0.52 | 0.92 | 0.83 | 1.63 | 0.55 | 0.28 |
| 1 | 1 | 370 | 3 | 319 | 0.02 | -0.37 | -0.78 | -0.41 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 371 | 4 | 881 | 0.09 | 1.87 | 0.92 | 0.78 | 1.63 | -0.61 | 0.26 |
| 1 | 1 | 372 | 3 | 195 | 0.03 | -0.48 | -1.20 | -1.12 | -0.72 | -0.61 | 0.18 |
| 1 | 1 | 373 | 2 | 666 | 0.08 | 1.11 | 0.92 | -0.62 | -0.72 | 0.55 | -1.37 |
| 1 | 1 | 374 | 4 | 189 | 0.06 | -1.09 | 0.09 | -0.68 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 375 | 3 | 467 | 0.09 | 0.10 | -1.06 | 0.03 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 376 | 2 | 293 | 0.03 | -0.80 | -1.20 | -0.68 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 377 | 5 | 413 | 0.1 | 0.86 | -0.78 | -0.36 | -0.72 | -0.61 | 0.13 |
| 1 | 1 | 378 | 10 | 781 | 0.06 | 0.27 | -0.78 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 379 | 8 | 916 | 0.13 | 1.19 | 0.09 | -0.55 | -0.33 | 2.88 | -2.30 |
| 1 | 1 | 380 | 3 | 130 | 0.02 | -1.27 | -0.78 | -1.15 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 381 | 8 | 657 | 0.12 | 0.89 | 0.09 | -0.17 | 0.06 | 0.55 | 0.94 |
| 1 | 1 | 382 | 10 | 947 | 0.53 | 1.65 | 0.07 | 4.82 | -0.09 | -0.38 | 0.49 |
| 1 | 1 | 383 | 3 | 879 | 0.03 | 0.38 | 0.92 | 0.81 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 384 | 4 | 56 | 0.11 | -1.22 | -1.20 | -1.17 | -1.01 | 0.55 | 0.18 |
| 1 | 1 | 385 | 5 | 447 | 0.14 | 1.16 | 0.59 | -0.92 | -0.72 | -0.61 | -2.32 |
| 1 | 1 | 386 | 4 | 194 | 0.03 | -0.72 | -0.78 | -1.13 | -0.72 | -0.61 | 0.84 |
| 1 | 1 | 387 | 3 | 709 | 0.01 | 0.09 | -0.78 | 0.82 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 388 | 3 | 618 | 0.07 | 0.15 | 0.09 | 0.03 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 389 | 3 | 120 | 0.05 | -1.52 | -0.78 | -1.01 | -0.72 | -0.61 | 0.91 |
| 1 | 1 | 390 | 3 | 388 | 0.02 | -0.38 | 0.92 | -0.68 | -0.72 | -0.61 | 0.40 |
| 1 | 1 | 391 | 8 | 549 | 0.11 | 0.19 | -0.83 | -0.34 | 0.06 | 0.55 | -1.67 |
| 1 | 1 | 392 | 13 | 799 | 0.17 | 0.63 | -0.91 | 0.76 | 1.63 | 0.55 | -0.07 |
| 1 | 1 | 393 | 5 | 724 | 0.02 | 0.34 | -0.78 | 0.82 | 1.63 | -0.61 | 0.43 |
| 1 | 1 | 394 | 5 | 679 | 0.06 | 0.56 | 0.92 | 0.85 | 0.06 | -0.61 | 0.37 |
| 1 | 1 | 395 | 6 | 254 | 0.01 | -0.76 | -0.78 | -0.68 | -0.72 | -0.61 | 0.37 |
| 1 | 1 | 396 | 4 | 622 | 0.08 | 0.28 | 0.09 | 0.82 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 397 | 8 | 533 | 0.1 | -0.29 | -0.83 | 0.82 | 0.06 | -0.61 | -0.63 |
| 1 | 1 | 398 | 7 | 245 | 0.05 | -0.30 | -0.78 | -1.13 | -0.72 | -0.61 | 0.76 |
| 1 | 1 | 399 | 6 | 316 | 0.1 | -1.04 | -0.92 | 0.32 | -0.72 | -0.61 | 0.59 |
| 1 | 1 | 400 | 3 | 235 | 0.02 | -0.39 | -1.20 | -0.89 | -0.72 | -0.61 | 0.41 |
| 1 | 1 | 401 | 1 | 62 | 0 | -1.89 | -0.78 | -0.50 | -1.11 | -0.61 | -0.62 |
| 1 | 1 | 402 | 3 | 931 | 0.08 | 0.33 | 0.92 | 1.78 | 0.06 | 2.88 | -0.92 |
| 1 | 1 | 403 | 2 | 502 | 0.01 | -0.09 | -0.78 | 0.33 | 0.06 | -0.61 | 0.63 |
| 1 | 1 | 404 | 3 | 9 | 0.04 | -0.69 | -0.78 | -0.08 | -0.72 | 2.88 | 1.52 |
| 1 | 1 | 405 | 9 | 310 | 0.05 | -0.59 | -0.78 | -0.63 | -0.72 | 0.55 | 0.44 |
| 1 | 1 | 406 | 2 | 6 | 0.04 | -0.30 | 2.67 | -1.18 | -1.11 | 0.55 | 1.27 |
| 1 | 1 | 407 | 5 | 669 | 0.12 | 1.45 | 0.92 | -0.32 | -0.72 | 0.55 | 0.41 |
| 1 | 1 | 408 | 5 | 993 | 0.04 | 1.91 | 2.67 | 3.06 | 3.19 | -0.61 | 0.07 |
| 1 | 1 | 409 | 4 | 777 | 0.02 | 1.09 | -0.78 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 410 | 7 | 688 | 0.13 | 0.79 | 0.92 | -0.08 | 0.06 | 0.55 | -0.09 |
| 1 | 1 | 411 | 4 | 381 | 0.1 | -1.02 | 0.92 | -0.64 | -0.72 | 0.55 | -0.68 |
| 1 | 1 | 412 | 4 | 901 | 0.24 | 0.64 | 0.92 | 3.32 | 0.06 | -0.03 | 0.38 |
| 1 | 1 | 413 | 12 | 847 | 0.29 | -0.15 | 0.50 | 3.06 | 0.06 | -0.23 | -0.88 |
| 1 | 1 | 414 | 3 | 695 | 0.07 | 0.84 | 0.92 | 0.83 | 0.06 | -0.61 | 0.63 |
| 1 | 1 | 415 | 4 | 670 | 0.06 | -0.42 | 0.92 | 0.86 | 0.06 | 0.55 | -0.02 |
| 1 | 1 | 416 | 9 | 791 | 0.2 | 0.34 | 2.67 | 0.31 | -0.02 | 0.55 | -0.30 |
| 1 | 1 | 417 | 5 | 570 | 0.05 | -0.48 | 0.09 | 0.82 | 0.06 | -0.61 | -0.80 |
| 1 | 1 | 418 | 9 | 131 | 0.04 | -0.96 | -0.78 | -1.18 | -1.11 | -0.61 | 0.58 |
| 1 | 1 | 419 | 6 | 291 | 0.04 | -0.07 | -1.20 | -0.53 | 0.06 | -0.61 | -2.29 |
| 1 | 1 | 420 | 6 | 717 | 0.13 | 0.31 | 2.67 | 0.26 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 421 | 3 | 432 | 0.07 | -1.02 | 0.09 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 422 | 4 | 602 | 0.03 | -0.07 | 0.92 | -0.50 | 0.06 | 0.55 | 0.87 |
| 1 | 1 | 423 | 4 | 662 | 0.15 | 0.09 | 0.09 | 0.02 | -0.72 | 2.88 | 0.46 |
| 1 | 1 | 424 | 5 | 159 | 0.09 | -0.88 | 0.09 | -1.18 | -1.11 | 0.55 | 0.63 |
| 1 | 1 | 425 | 5 | 586 | 0.03 | -0.24 | 0.92 | 0.31 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 426 | 6 | 841 | 0.06 | 1.12 | 0.92 | 0.83 | 1.63 | -0.61 | 0.24 |
| 1 | 1 | 427 | 6 | 418 | 0.09 | -0.70 | 0.92 | -0.63 | -0.72 | 0.55 | 0.03 |
| 1 | 1 | 428 | 8 | 806 | 0.16 | 2.10 | 0.92 | 0.77 | -0.72 | 0.55 | 0.42 |
| 1 | 1 | 429 | 5 | 667 | 0.04 | -0.34 | 0.92 | 0.30 | 0.06 | 0.55 | -1.30 |
| 1 | 1 | 430 | 3 | 591 | 0.05 | 0.04 | 0.09 | -0.08 | 0.06 | 0.55 | 0.12 |
| 1 | 1 | 431 | 2 | 773 | 0.05 | 1.01 | -0.78 | -0.05 | 1.63 | 0.55 | 0.87 |
| 1 | 1 | 432 | 5 | 957 | 0.14 | 2.05 | 0.92 | 0.55 | 1.63 | 2.88 | -0.32 |
| 1 | 1 | 433 | 9 | 939 | 0.12 | 1.25 | 2.67 | 0.81 | 1.63 | 0.55 | 0.95 |
| 1 | 1 | 434 | 5 | 135 | 0.04 | -0.72 | -0.78 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 435 | 2 | 744 | 0.01 | 0.63 | -0.78 | 0.82 | 1.63 | -0.61 | 0.04 |
| 1 | 1 | 436 | 5 | 834 | 0.16 | 0.69 | -0.43 | -0.53 | 1.63 | 0.55 | -2.12 |
| 1 | 1 | 437 | 4 | 48 | 0.02 | -0.47 | -0.78 | -1.08 | -0.72 | -0.61 | -2.30 |
| 1 | 1 | 438 | 7 | 656 | 0.04 | 0.68 | 0.92 | 0.33 | 0.06 | -0.61 | 0.55 |
| 1 | 1 | 439 | 3 | 330 | 0.04 | -1.27 | 0.09 | -0.08 | -0.72 | -0.61 | 0.44 |
| 1 | 1 | 440 | 6 | 168 | 0.04 | -0.62 | -0.78 | -1.18 | -1.11 | -0.61 | 0.56 |
| 1 | 1 | 441 | 10 | 46 | 0.15 | -0.03 | 2.67 | -0.93 | -0.91 | -0.61 | 1.01 |
| 1 | 1 | 442 | 5 | 239 | 0.1 | -0.52 | -0.87 | -1.08 | -0.72 | 0.55 | 0.65 |
| 1 | 1 | 443 | 3 | 921 | 0.17 | 0.60 | 2.67 | 0.50 | 1.63 | 0.55 | -0.04 |
| 1 | 1 | 444 | 6 | 70 | 0.06 | -1.61 | -1.20 | -1.16 | -0.72 | -0.61 | 0.91 |
| 1 | 1 | 445 | 7 | 729 | 0.06 | -0.28 | 0.92 | 1.78 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 446 | 5 | 913 | 0.13 | 1.41 | 0.92 | 0.66 | 0.06 | 2.88 | 0.42 |
| 1 | 1 | 447 | 5 | 919 | 0.11 | 1.43 | 0.92 | -0.53 | 1.63 | 0.55 | -2.12 |
| 1 | 1 | 448 | 5 | 298 | 0.01 | -0.26 | -0.78 | -0.68 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 449 | 5 | 989 | 0.29 | 3.64 | 0.59 | 3.44 | 1.63 | -0.61 | 0.79 |
| 1 | 1 | 450 | 4 | 654 | 0.06 | 0.87 | 0.09 | -0.25 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 451 | 5 | 243 | 0.06 | -0.53 | -0.78 | -0.98 | -0.72 | -0.61 | 0.63 |
| 1 | 1 | 452 | 3 | 289 | 0.03 | -0.20 | -1.20 | -0.58 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 453 | 8 | 119 | 0.04 | -1.23 | -1.20 | -1.12 | -0.72 | -0.61 | 0.58 |
| 1 | 1 | 454 | 7 | 639 | 0.09 | -0.51 | 0.92 | 0.10 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 455 | 4 | 941 | 0.17 | 0.74 | 1.03 | 0.58 | 1.63 | 2.88 | -0.63 |
| 1 | 1 | 456 | 4 | 75 | 0.06 | -1.27 | -0.78 | -1.18 | -1.11 | 0.55 | 0.26 |
| 1 | 1 | 457 | 3 | 339 | 0.09 | 0.21 | -0.92 | -0.63 | -0.72 | -0.61 | 0.54 |
| 1 | 1 | 458 | 5 | 347 | 0.11 | -1.20 | -0.95 | -0.08 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 459 | 12 | 496 | 0.06 | -0.64 | -0.78 | 0.82 | 0.06 | -0.61 | -0.75 |
| 1 | 1 | 460 | 12 | 627 | 0.09 | -0.53 | 0.92 | 0.84 | 0.06 | -0.61 | -0.77 |
| 1 | 1 | 461 | 4 | 303 | 0.03 | -0.51 | 0.92 | -1.18 | -1.11 | -0.61 | 0.31 |
| 1 | 1 | 462 | 7 | 559 | 0.06 | 0.01 | -0.78 | 0.33 | 0.06 | 0.55 | 0.72 |
| 1 | 1 | 463 | 6 | 68 | 0.06 | -1.10 | -0.78 | -0.96 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 464 | 6 | 191 | 0.04 | -0.83 | 0.09 | -1.18 | -1.11 | -0.61 | 0.44 |
| 1 | 1 | 465 | 4 | 267 | 0.04 | -0.30 | -0.78 | -0.63 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 466 | 5 | 10 | 0.13 | 0.22 | -0.87 | -0.77 | -0.72 | 2.88 | 1.42 |
| 1 | 1 | 467 | 5 | 634 | 0.05 | 0.28 | 0.09 | 0.33 | 0.06 | 0.55 | 0.65 |
| 1 | 1 | 468 | 13 | 594 | 0.07 | -0.20 | 0.92 | 0.32 | 0.06 | -0.61 | -0.37 |
| 1 | 1 | 469 | 6 | 730 | 0.04 | 0.80 | -0.78 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 470 | 3 | 956 | 0.01 | 0.97 | -0.78 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 471 | 4 | 515 | 0.07 | -0.75 | 0.92 | -0.08 | 0.06 | -0.61 | 0.17 |
| 1 | 1 | 472 | 9 | 535 | 0.14 | 0.15 | 1.02 | 0.19 | -0.72 | -0.61 | 0.54 |
| 1 | 1 | 473 | 4 | 522 | 0.02 | 0.50 | -0.78 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 474 | 10 | 450 | 0.1 | -0.55 | -0.78 | 0.29 | 0.06 | -0.61 | -0.31 |
| 1 | 1 | 475 | 2 | 737 | 0.01 | 0.38 | -1.20 | 0.82 | 1.63 | -0.61 | 0.70 |
| 1 | 1 | 476 | 4 | 800 | 0.15 | 1.60 | 0.92 | 0.35 | 0.06 | 0.55 | 1.39 |
| 1 | 1 | 477 | 3 | 475 | 0.05 | -0.19 | 0.92 | -0.63 | -0.72 | 0.55 | -0.50 |
| 1 | 1 | 478 | 2 | 787 | 0.03 | 2.39 | 0.92 | 0.46 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 479 | 5 | 980 | 0.03 | 1.12 | -0.78 | 3.06 | 3.19 | 0.55 | -1.30 |
| 1 | 1 | 480 | 3 | 91 | 0.05 | -1.69 | -0.78 | -0.08 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 481 | 5 | 504 | 0.05 | 0.61 | 0.92 | -0.57 | -0.72 | -0.61 | -0.41 |
| 1 | 1 | 482 | 4 | 569 | 0.11 | 0.29 | -0.89 | -0.18 | 0.06 | 0.55 | -1.08 |
| 1 | 1 | 483 | 2 | 904 | 0.02 | 2.21 | 0.92 | 0.68 | 1.63 | -0.61 | 0.87 |
| 1 | 1 | 484 | 3 | 915 | 0.1 | 0.73 | 0.92 | 0.86 | 0.06 | 2.88 | 1.35 |
| 1 | 1 | 485 | 5 | 973 | 0.04 | 1.49 | 0.92 | 3.06 | 3.19 | -0.61 | 0.07 |
| 1 | 1 | 486 | 9 | 377 | 0.06 | -0.06 | -0.78 | -0.08 | -0.72 | -0.61 | 0.53 |
| 1 | 1 | 487 | 4 | 608 | 0.12 | 0.44 | 0.92 | -0.19 | 0.06 | -0.61 | -0.05 |
| 1 | 1 | 488 | 6 | 399 | 0.03 | 0.23 | -0.78 | -0.52 | 0.06 | -0.61 | -2.22 |
| 1 | 1 | 489 | 8 | 482 | 0.08 | -0.13 | 0.92 | -0.60 | -0.72 | 0.55 | 0.53 |
| 1 | 1 | 490 | 9 | 442 | 0.05 | 0.26 | 0.92 | -0.68 | -0.72 | -0.61 | 0.10 |
| 1 | 1 | 491 | 3 | 147 | 0.02 | -1.17 | 0.09 | -1.18 | -1.11 | -0.61 | 0.37 |
| 1 | 1 | 492 | 6 | 614 | 0.06 | -0.27 | 0.92 | -0.08 | 0.06 | 0.55 | 0.46 |
| 1 | 1 | 493 | 3 | 150 | 0.02 | -1.30 | -0.78 | -1.15 | -0.72 | -0.61 | 0.14 |
| 1 | 1 | 494 | 8 | 520 | 0.02 | 0.08 | -0.78 | 0.33 | 0.06 | -0.61 | 0.81 |
| 1 | 1 | 495 | 3 | 981 | 0.07 | 1.52 | 0.92 | 3.06 | 3.19 | 0.55 | 0.08 |
| 1 | 1 | 496 | 8 | 7 | 0.04 | -1.20 | -0.78 | -1.29 | -1.11 | -0.61 | -2.28 |
| 1 | 1 | 497 | 1 | 890 | 0 | 0.04 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 498 | 3 | 300 | 0.02 | 0.18 | -0.78 | -1.12 | -0.72 | -0.61 | 0.11 |
| 1 | 1 | 499 | 6 | 109 | 0.08 | -1.71 | -0.78 | -0.64 | -0.72 | -0.61 | -0.77 |
| 1 | 1 | 500 | 1 | 498 | 0 | 1.23 | 0.09 | -0.55 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 501 | 2 | 122 | 0.01 | -1.59 | -0.78 | -1.08 | -0.72 | -0.61 | 0.63 |
| 1 | 1 | 502 | 8 | 971 | 0.23 | 3.06 | 0.92 | -0.06 | 1.63 | 2.88 | -0.44 |
| 1 | 1 | 503 | 3 | 466 | 0.07 | -0.15 | -0.92 | 0.32 | -0.72 | 2.88 | 0.50 |
| 1 | 1 | 504 | 8 | 546 | 0.06 | -0.19 | -0.78 | 0.33 | 0.06 | 0.55 | 0.34 |
| 1 | 1 | 505 | 9 | 360 | 0.13 | -1.19 | -0.97 | -0.08 | 0.06 | -0.61 | 0.35 |
| 1 | 1 | 506 | 5 | 518 | 0.12 | -0.88 | 0.92 | 0.16 | -0.72 | 0.55 | -0.56 |
| 1 | 1 | 507 | 3 | 499 | 0.01 | -0.10 | -0.78 | 0.33 | 0.06 | -0.61 | 0.49 |
| 1 | 1 | 508 | 12 | 645 | 0.08 | 0.64 | 0.92 | -0.59 | 0.06 | -0.61 | -2.28 |
| 1 | 1 | 509 | 5 | 641 | 0.11 | -0.57 | 0.09 | 0.85 | 0.06 | 0.55 | -0.89 |
| 1 | 1 | 510 | 1 | 359 | 0 | -0.30 | -1.20 | -0.68 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 511 | 7 | 92 | 0.03 | -1.04 | -1.20 | -1.18 | -1.11 | -0.61 | 0.09 |
| 1 | 1 | 512 | 3 | 732 | 0.01 | 0.71 | -1.20 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 513 | 9 | 95 | 0.06 | -0.13 | 0.09 | -1.01 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 514 | 4 | 663 | 0.02 | 0.57 | 0.09 | -0.50 | 0.06 | 0.55 | -2.20 |
| 1 | 1 | 515 | 2 | 362 | 0.07 | -0.37 | -0.78 | 0.34 | -0.72 | -0.61 | -1.37 |
| 1 | 1 | 516 | 10 | 270 | 0.12 | 0.58 | -0.91 | -0.75 | -0.72 | 2.88 | 0.58 |
| 1 | 1 | 517 | 3 | 740 | 0.03 | 0.35 | -1.20 | 0.84 | 1.63 | -0.61 | 0.96 |
| 1 | 1 | 518 | 5 | 73 | 0.08 | -1.34 | -0.87 | -0.69 | -0.72 | 0.55 | 1.52 |
| 1 | 1 | 519 | 6 | 651 | 0.08 | 0.91 | 0.09 | -0.20 | 0.06 | 0.55 | 0.27 |
| 1 | 1 | 520 | 15 | 861 | 0.16 | 0.86 | 0.92 | -0.03 | 0.06 | 2.88 | 0.82 |
| 1 | 1 | 521 | 2 | 933 | 0.01 | 1.30 | 2.67 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 522 | 6 | 759 | 0.1 | 0.68 | -0.92 | 0.46 | 1.63 | -0.61 | -1.08 |
| 1 | 1 | 523 | 5 | 776 | 0.04 | 0.78 | 0.09 | 0.82 | 1.63 | -0.61 | 0.40 |
| 1 | 1 | 524 | 1 | 900 | 0 | 0.39 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 525 | 7 | 658 | 0.1 | -0.14 | -0.78 | 0.04 | 0.06 | 2.88 | 0.57 |
| 1 | 1 | 526 | 5 | 873 | 0.14 | 0.95 | 0.09 | -0.50 | 0.06 | 2.88 | -1.43 |
| 1 | 1 | 527 | 5 | 30 | 0.13 | -0.44 | -0.95 | -0.73 | -0.72 | 2.88 | 0.16 |
| 1 | 1 | 528 | 2 | 509 | 0.06 | -0.96 | 0.09 | -0.08 | 0.06 | 0.55 | 0.08 |
| 1 | 1 | 529 | 3 | 807 | 0.07 | 1.80 | 0.92 | -0.63 | -0.72 | 0.55 | -2.31 |
| 1 | 1 | 530 | 2 | 551 | 0.02 | 0.50 | -0.78 | 0.33 | 0.06 | -0.61 | 0.56 |
| 1 | 1 | 531 | 4 | 986 | 0.04 | 1.24 | 0.92 | 3.06 | 3.19 | 0.55 | -0.84 |
| 1 | 1 | 532 | 5 | 480 | 0.08 | -0.85 | -0.78 | 0.24 | 0.06 | 0.55 | -0.33 |
| 1 | 1 | 533 | 4 | 550 | 0.04 | -0.94 | 0.92 | 0.32 | 0.06 | -0.61 | -0.78 |
| 1 | 1 | 534 | 2 | 34 | 0.04 | -2.18 | -0.78 | -1.21 | -1.11 | -0.61 | 0.70 |
| 1 | 1 | 535 | 4 | 178 | 0.07 | -1.25 | -0.78 | -0.66 | -0.72 | 0.55 | 0.85 |
| 1 | 1 | 536 | 5 | 960 | 0.03 | 1.03 | -0.78 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 537 | 4 | 784 | 0.1 | 1.07 | -0.78 | 0.20 | 1.63 | 0.55 | 0.07 |
| 1 | 1 | 538 | 2 | 979 | 0.01 | 1.72 | 0.92 | 3.06 | 3.19 | -0.61 | -0.30 |
| 1 | 1 | 539 | 4 | 31 | 0.03 | -1.14 | -0.78 | -1.18 | -1.11 | -0.61 | -1.67 |
| 1 | 1 | 540 | 6 | 511 | 0.11 | -0.39 | -0.99 | 0.83 | 0.06 | -0.61 | 0.42 |
| 1 | 1 | 541 | 2 | 102 | 0.01 | -1.81 | -1.20 | -0.68 | -0.72 | -0.61 | 0.42 |
| 1 | 1 | 542 | 1 | 917 | 0 | 2.72 | 0.92 | 0.68 | 1.63 | -0.61 | 0.04 |
| 1 | 1 | 543 | 5 | 371 | 0.04 | -0.71 | 0.09 | -0.08 | -0.72 | -0.61 | 0.63 |
| 1 | 1 | 544 | 5 | 76 | 0.04 | -1.49 | -0.78 | -1.18 | -1.11 | -0.61 | 0.82 |
| 1 | 1 | 545 | 3 | 772 | 0.01 | 1.05 | -0.78 | 0.82 | 1.63 | -0.61 | 0.04 |
| 1 | 1 | 546 | 1 | 721 | 0 | 0.26 | 0.09 | -0.69 | 0.06 | 2.88 | 1.01 |
| 1 | 1 | 547 | 4 | 907 | 0.06 | 1.73 | 0.92 | 0.65 | 1.63 | 0.55 | -0.44 |
| 1 | 1 | 548 | 4 | 283 | 0.04 | 0.13 | 0.09 | -0.69 | -0.72 | -0.61 | -2.30 |
| 1 | 1 | 549 | 8 | 346 | 0.05 | -0.83 | 0.92 | -0.73 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 550 | 10 | 930 | 0.18 | 1.28 | 0.92 | -0.54 | -0.09 | 2.88 | -2.18 |
| 1 | 1 | 551 | 3 | 631 | 0.07 | 0.54 | -0.78 | 0.84 | 0.06 | -0.61 | -1.23 |
| 1 | 1 | 552 | 4 | 503 | 0.1 | -0.33 | 0.09 | 0.02 | 0.06 | -0.61 | 0.54 |
| 1 | 1 | 553 | 7 | 155 | 0.05 | -0.77 | -0.78 | -0.95 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 554 | 4 | 699 | 0.08 | 1.11 | 0.92 | 0.12 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 555 | 5 | 857 | 0.06 | 1.05 | 0.92 | 0.32 | 1.63 | 0.55 | 0.89 |
| 1 | 1 | 556 | 1 | 595 | 0 | 0.50 | 0.09 | -0.08 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 557 | 4 | 768 | 0.03 | 0.80 | -1.20 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 558 | 2 | 959 | 0.22 | 1.17 | 2.67 | 3.06 | 0.85 | -0.61 | -0.11 |
| 1 | 1 | 559 | 4 | 484 | 0.09 | -0.77 | -0.89 | 0.32 | 0.06 | 0.55 | 0.95 |
| 1 | 1 | 560 | 4 | 700 | 0.03 | 1.29 | 0.92 | 0.32 | 0.06 | -0.61 | 0.89 |
| 1 | 1 | 561 | 1 | 685 | 0 | 0.87 | 0.09 | -0.89 | 0.06 | 0.55 | -2.29 |
| 1 | 1 | 562 | 6 | 998 | 0.29 | 1.11 | -0.49 | 3.06 | 3.19 | 2.88 | -1.07 |
| 1 | 1 | 563 | 7 | 318 | 0.11 | -0.85 | 0.09 | -0.47 | -0.72 | -0.61 | -0.66 |
| 1 | 1 | 564 | 5 | 103 | 0.07 | -1.28 | -0.78 | -0.80 | -0.72 | -0.61 | -1.21 |
| 1 | 1 | 565 | 19 | 495 | 0.13 | 0.23 | -0.78 | -0.64 | 0.06 | 0.55 | -2.31 |
| 1 | 1 | 566 | 4 | 134 | 0.14 | -0.77 | 0.09 | -0.98 | -0.91 | 0.55 | -1.28 |
| 1 | 1 | 567 | 13 | 795 | 0.11 | 0.78 | -0.94 | -0.08 | 1.63 | -0.61 | -2.26 |
| 1 | 1 | 568 | 6 | 948 | 0.11 | 1.17 | 0.92 | 3.38 | 1.63 | 0.55 | 0.14 |
| 1 | 1 | 569 | 8 | 839 | 0.18 | 2.26 | 0.81 | 0.54 | 0.06 | -0.61 | -1.45 |
| 1 | 1 | 570 | 4 | 206 | 0.06 | -0.90 | -0.78 | -0.61 | -0.72 | -0.61 | -0.84 |
| 1 | 1 | 571 | 4 | 244 | 0.05 | -1.18 | -0.78 | -0.08 | -0.72 | -0.61 | -0.67 |
| 1 | 1 | 572 | 2 | 5 | 0.09 | -1.04 | -0.78 | -0.60 | -0.91 | 2.88 | -0.73 |
| 1 | 1 | 573 | 2 | 601 | 0.03 | -0.40 | -0.78 | 0.85 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 574 | 3 | 82 | 0.01 | -0.30 | -0.78 | -0.50 | -0.72 | 0.55 | -2.35 |
| 1 | 1 | 575 | 2 | 785 | 0.01 | 1.53 | 0.92 | 0.81 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 576 | 4 | 276 | 0.08 | -1.29 | 0.09 | -0.55 | -0.72 | -0.61 | 0.30 |
| 1 | 1 | 577 | 1 | 745 | 0 | 1.73 | 0.09 | 0.45 | 0.06 | 0.55 | 0.87 |
| 1 | 1 | 578 | 4 | 675 | 0.03 | -0.29 | 0.92 | 0.87 | 0.06 | 0.55 | 0.47 |
| 1 | 1 | 579 | 1 | 674 | 0 | -0.36 | 0.92 | 1.78 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 580 | 2 | 79 | 0.03 | -1.81 | -1.20 | -0.68 | -0.72 | -0.61 | -0.46 |
| 1 | 1 | 581 | 7 | 419 | 0.07 | 0.47 | -0.78 | -0.08 | -0.72 | -0.61 | 0.34 |
| 1 | 1 | 582 | 6 | 212 | 0.04 | -0.73 | -1.20 | -0.68 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 583 | 4 | 64 | 0.04 | -1.10 | 0.09 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 584 | 5 | 389 | 0.07 | -0.20 | -0.78 | -0.65 | 0.06 | -0.61 | -0.01 |
| 1 | 1 | 585 | 2 | 11 | 0.03 | -0.89 | -0.78 | -1.29 | -1.11 | -0.61 | -2.28 |
| 1 | 1 | 586 | 2 | 605 | 0.14 | 0.72 | 0.09 | -0.16 | -0.72 | 0.55 | -1.37 |
| 1 | 1 | 587 | 5 | 321 | 0.06 | -1.21 | 0.92 | -0.65 | -0.72 | -0.61 | -0.45 |
| 1 | 1 | 588 | 7 | 950 | 0.29 | 2.55 | -0.78 | -0.06 | 1.63 | 2.88 | -0.28 |
| 1 | 1 | 589 | 3 | 397 | 0.02 | 0.05 | 0.92 | -1.12 | -0.72 | -0.61 | 0.12 |
| 1 | 1 | 590 | 3 | 545 | 0.02 | 0.40 | -0.78 | 0.33 | 0.06 | -0.61 | 0.83 |
| 1 | 1 | 591 | 3 | 477 | 0.11 | -0.02 | -1.06 | 0.10 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 592 | 5 | 140 | 0.04 | -0.53 | 1.38 | -1.18 | -1.11 | -0.61 | 1.01 |
| 1 | 1 | 593 | 4 | 690 | 0.04 | 0.01 | 0.92 | 0.86 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 594 | 3 | 505 | 0.07 | 0.66 | 0.92 | -0.54 | -0.72 | -0.61 | 0.12 |
| 1 | 1 | 595 | 2 | 644 | 0.01 | 0.34 | 0.09 | -0.50 | 0.06 | 0.55 | -2.23 |
| 1 | 1 | 596 | 7 | 110 | 0.04 | -1.23 | -0.78 | -1.18 | -1.11 | -0.61 | 0.39 |
| 1 | 1 | 597 | 5 | 137 | 0.03 | -0.99 | -0.78 | -1.18 | -1.11 | -0.61 | 0.30 |
| 1 | 1 | 598 | 4 | 736 | 0.09 | 0.55 | 0.92 | 0.83 | 0.06 | 0.55 | 1.14 |
| 1 | 1 | 599 | 3 | 392 | 0.13 | -0.94 | 0.92 | -0.48 | -0.72 | 0.55 | 1.35 |
| 1 | 1 | 600 | 6 | 299 | 0.02 | -0.25 | -0.78 | -0.68 | -0.72 | -0.61 | 0.24 |
| 1 | 1 | 601 | 2 | 803 | 0.05 | 0.13 | 1.15 | 0.34 | 1.63 | 0.55 | 0.47 |
| 1 | 1 | 602 | 4 | 668 | 0.09 | 0.69 | 0.09 | 0.74 | 0.06 | -0.61 | -1.02 |
| 1 | 1 | 603 | 6 | 760 | 0.03 | 0.87 | -0.78 | 0.82 | 1.63 | -0.61 | 0.14 |
| 1 | 1 | 604 | 8 | 758 | 0.11 | 0.61 | 2.67 | 0.18 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 605 | 5 | 497 | 0.03 | -0.18 | -0.78 | 0.33 | 0.06 | -0.61 | -0.38 |
| 1 | 1 | 606 | 6 | 770 | 0.02 | 0.92 | -0.78 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 607 | 4 | 35 | 0.04 | -0.92 | -0.78 | -1.10 | -0.72 | -0.61 | -2.31 |
| 1 | 1 | 608 | 10 | 486 | 0.04 | -0.12 | -1.20 | 0.33 | 0.06 | -0.61 | 0.76 |
| 1 | 1 | 609 | 7 | 217 | 0.06 | -0.70 | -0.78 | -0.57 | -0.72 | -0.61 | -1.14 |
| 1 | 1 | 610 | 2 | 21 | 0.01 | -1.56 | -1.20 | -1.18 | -1.11 | -0.61 | -1.08 |
| 1 | 1 | 611 | 1 | 610 | 0 | 0.67 | 0.09 | -0.08 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 612 | 7 | 820 | 0.05 | 1.33 | 0.09 | 0.82 | 1.63 | -0.61 | 0.57 |
| 1 | 1 | 613 | 5 | 629 | 0.06 | 0.52 | 0.09 | -0.10 | 0.06 | 0.55 | 0.10 |
| 1 | 1 | 614 | 4 | 779 | 0.1 | 0.61 | -0.78 | -0.50 | 0.06 | 2.88 | -1.37 |
| 1 | 1 | 615 | 4 | 836 | 0.06 | 1.13 | 0.09 | 0.46 | 1.63 | -0.61 | -1.37 |
| 1 | 1 | 616 | 2 | 132 | 0.02 | -1.23 | -0.78 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 617 | 20 | 468 | 0.09 | 0.17 | 0.09 | -0.56 | 0.06 | -0.61 | -2.29 |
| 1 | 1 | 618 | 6 | 83 | 0.03 | -1.17 | -1.20 | -1.18 | -1.11 | -0.61 | 0.58 |
| 1 | 1 | 619 | 5 | 516 | 0.1 | -0.14 | 0.92 | -0.59 | 0.06 | -0.61 | 0.18 |
| 1 | 1 | 620 | 3 | 256 | 0.02 | -0.88 | -0.78 | -0.54 | -0.72 | -0.61 | 0.08 |
| 1 | 1 | 621 | 5 | 402 | 0.09 | 0.18 | 0.92 | -1.15 | -0.72 | -0.61 | 0.75 |
| 1 | 1 | 622 | 8 | 720 | 0.15 | 0.56 | 0.92 | 0.87 | -0.03 | 0.55 | 0.51 |
| 1 | 1 | 623 | 6 | 671 | 0.07 | 0.30 | 0.92 | 0.82 | 0.06 | -0.61 | -0.59 |
| 1 | 1 | 624 | 3 | 338 | 0.02 | 0.16 | -0.78 | -0.68 | -0.72 | -0.61 | 0.19 |
| 1 | 1 | 625 | 5 | 902 | 0.14 | 1.42 | 0.92 | 0.15 | 1.63 | 0.55 | -1.20 |
| 1 | 1 | 626 | 7 | 451 | 0.09 | 0.59 | -0.78 | -0.41 | -0.72 | 0.55 | -0.19 |
| 1 | 1 | 627 | 9 | 269 | 0.07 | -0.88 | -0.78 | -0.66 | -0.72 | 0.55 | 0.23 |
| 1 | 1 | 628 | 3 | 485 | 0.06 | 0.74 | -0.78 | -0.53 | 0.06 | -0.61 | 0.30 |
| 1 | 1 | 629 | 6 | 506 | 0.02 | -0.05 | -0.78 | 0.33 | 0.06 | -0.61 | 0.35 |
| 1 | 1 | 630 | 3 | 510 | 0.04 | -0.88 | 0.92 | -0.08 | 0.06 | -0.61 | -0.51 |
| 1 | 1 | 631 | 4 | 850 | 0.1 | 0.55 | 2.67 | 0.82 | 0.06 | 0.55 | -0.21 |
| 1 | 1 | 632 | 9 | 571 | 0.12 | 0.74 | 0.09 | -0.64 | 0.06 | -0.61 | -2.31 |
| 1 | 1 | 633 | 11 | 819 | 0.16 | 0.15 | 2.67 | 0.22 | 0.06 | 0.55 | 1.43 |
| 1 | 1 | 634 | 6 | 334 | 0.06 | -0.24 | 0.92 | -1.18 | -1.11 | -0.61 | 0.56 |
| 1 | 1 | 635 | 3 | 735 | 0.02 | 0.51 | -0.78 | 0.82 | 1.63 | -0.61 | 0.33 |
| 1 | 1 | 636 | 6 | 454 | 0.13 | -0.98 | 0.92 | 0.60 | -0.72 | -0.61 | -0.84 |
| 1 | 1 | 637 | 2 | 356 | 0.03 | 0.21 | 0.09 | -1.18 | -0.72 | -0.61 | 0.87 |
| 1 | 1 | 638 | 7 | 906 | 0.23 | 1.43 | 0.92 | 0.08 | 1.63 | -0.61 | -2.10 |
| 1 | 1 | 639 | 4 | 638 | 0.04 | -0.13 | 2.67 | 0.33 | -0.72 | -0.61 | -0.30 |
| 1 | 1 | 640 | 3 | 547 | 0.11 | 0.87 | 0.09 | -0.48 | -0.72 | 0.55 | 0.47 |
| 1 | 1 | 641 | 3 | 793 | 0.02 | 1.24 | -0.78 | 0.86 | 1.63 | -0.61 | 0.83 |
| 1 | 1 | 642 | 4 | 703 | 0.02 | 0.14 | 0.92 | 0.86 | 0.06 | 0.55 | -0.30 |
| 1 | 1 | 643 | 6 | 72 | 0.09 | -1.62 | -0.85 | -0.64 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 644 | 7 | 230 | 0.02 | -0.77 | -0.78 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 645 | 5 | 438 | 0.04 | -0.53 | -1.20 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 646 | 4 | 829 | 0.12 | 0.83 | 0.09 | 0.11 | 0.06 | 2.88 | 1.04 |
| 1 | 1 | 647 | 7 | 342 | 0.11 | -0.05 | -0.96 | -0.76 | -0.72 | 0.55 | 0.51 |
| 1 | 1 | 648 | 3 | 828 | 0.07 | 0.62 | 2.67 | 0.82 | 0.06 | -0.61 | 1.18 |
| 1 | 1 | 649 | 7 | 575 | 0.05 | -0.35 | -0.78 | 0.87 | 0.06 | 0.55 | 0.47 |
| 1 | 1 | 650 | 3 | 582 | 0.03 | 0.21 | -0.78 | 0.33 | 0.06 | 0.55 | 0.22 |
| 1 | 1 | 651 | 6 | 351 | 0.05 | -0.59 | -0.78 | -0.63 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 652 | 3 | 133 | 0.06 | -0.69 | -1.20 | -0.83 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 653 | 3 | 317 | 0.06 | -1.38 | 0.09 | -0.08 | -0.72 | -0.61 | -0.51 |
| 1 | 1 | 654 | 10 | 566 | 0.04 | -0.59 | 0.92 | 0.31 | 0.06 | -0.61 | -0.68 |
| 1 | 1 | 655 | 2 | 868 | 0.01 | 1.01 | 0.92 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 656 | 1 | 623 | 0 | 0.84 | -0.78 | -0.08 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 657 | 5 | 581 | 0.09 | -0.73 | 0.92 | -0.08 | 0.06 | 0.55 | 0.23 |
| 1 | 1 | 658 | 4 | 373 | 0.08 | -0.43 | 0.92 | -1.18 | -1.11 | 0.55 | -0.02 |
| 1 | 1 | 659 | 5 | 209 | 0.06 | -1.75 | -0.78 | -0.08 | -0.72 | -0.61 | 0.23 |
| 1 | 1 | 660 | 5 | 228 | 0.05 | -0.76 | -1.20 | -0.68 | -0.72 | 0.55 | 0.25 |
| 1 | 1 | 661 | 1 | 954 | 0 | 0.23 | 0.92 | 3.06 | 0.06 | 2.88 | -0.84 |
| 1 | 1 | 662 | 5 | 540 | 0.04 | 0.29 | -0.78 | 0.33 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 663 | 8 | 332 | 0.16 | -1.33 | -0.94 | -0.08 | 0.06 | -0.61 | -0.76 |
| 1 | 1 | 664 | 4 | 471 | 0.03 | -0.35 | -0.78 | 0.33 | 0.06 | -0.61 | 0.76 |
| 1 | 1 | 665 | 8 | 862 | 0.07 | 1.44 | 0.92 | -0.06 | 1.63 | 0.55 | 0.11 |
| 1 | 1 | 666 | 5 | 914 | 0.04 | 0.41 | 0.92 | 1.75 | 1.63 | 0.55 | -0.84 |
| 1 | 1 | 667 | 7 | 174 | 0.05 | -1.40 | -0.78 | -0.65 | -0.72 | -0.61 | -0.34 |
| 1 | 1 | 668 | 2 | 827 | 0.09 | 2.14 | 0.50 | -0.12 | 0.06 | -0.61 | -2.38 |
| 1 | 1 | 669 | 4 | 718 | 0.09 | 2.18 | 0.09 | 0.46 | 0.06 | -0.61 | 0.52 |
| 1 | 1 | 670 | 6 | 42 | 0.06 | -0.35 | -0.78 | -0.69 | -0.72 | 2.88 | 0.60 |
| 1 | 1 | 671 | 5 | 712 | 0.12 | 1.03 | 0.92 | -0.39 | -0.72 | 0.55 | -2.32 |
| 1 | 1 | 672 | 5 | 912 | 0.04 | 0.48 | 0.09 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 673 | 5 | 226 | 0.06 | -0.52 | -0.78 | -0.66 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 674 | 7 | 200 | 0.03 | -1.15 | -0.78 | -0.68 | -0.72 | -0.61 | 0.76 |
| 1 | 1 | 675 | 6 | 165 | 0.03 | -1.55 | -0.78 | -0.63 | -0.72 | -0.61 | 0.77 |
| 1 | 1 | 676 | 7 | 481 | 0.04 | 0.13 | -0.78 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 677 | 2 | 460 | 0.04 | -0.63 | 0.09 | 0.30 | -0.72 | 0.55 | 1.27 |
| 1 | 1 | 678 | 3 | 405 | 0.03 | 0.38 | 0.09 | -0.68 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 679 | 7 | 928 | 0.15 | 2.09 | 0.92 | 0.11 | 0.06 | 2.88 | 0.90 |
| 1 | 1 | 680 | 7 | 584 | 0.04 | 0.28 | 0.09 | 0.33 | 0.06 | -0.61 | 0.70 |
| 1 | 1 | 681 | 7 | 525 | 0.14 | -0.40 | 0.92 | -0.02 | -0.72 | 0.55 | 0.50 |
| 1 | 1 | 682 | 4 | 54 | 0.04 | -1.31 | -0.78 | -1.18 | -1.11 | 0.55 | 0.85 |
| 1 | 1 | 683 | 7 | 521 | 0.03 | 0.10 | -0.78 | 0.33 | 0.06 | -0.61 | 0.57 |
| 1 | 1 | 684 | 2 | 304 | 0.06 | -1.30 | -0.99 | -0.08 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 685 | 6 | 802 | 0.09 | 0.79 | -0.92 | 0.46 | 1.63 | -0.61 | -1.67 |
| 1 | 1 | 686 | 2 | 242 | 0.04 | -1.34 | 0.09 | -0.60 | -0.72 | -0.61 | 0.89 |
| 1 | 1 | 687 | 3 | 365 | 0.01 | -0.71 | 0.92 | -0.68 | -0.72 | -0.61 | 0.47 |
| 1 | 1 | 688 | 7 | 59 | 0.05 | -0.25 | -0.78 | -1.08 | -0.72 | -0.61 | -2.24 |
| 1 | 1 | 689 | 4 | 219 | 0.02 | -0.51 | 0.09 | -1.18 | -1.11 | -0.61 | 0.87 |
| 1 | 1 | 690 | 4 | 50 | 0.04 | -1.22 | -0.78 | -1.18 | -1.11 | -0.61 | -1.08 |
| 1 | 1 | 691 | 5 | 465 | 0.08 | -0.54 | 0.92 | 0.34 | -0.72 | -0.61 | 0.25 |
| 1 | 1 | 692 | 1 | 1000 | 0 | 4.25 | 0.92 | 3.06 | 1.63 | 2.88 | -0.44 |
| 1 | 1 | 693 | 2 | 918 | 0.02 | 0.66 | -0.78 | 3.06 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 694 | 7 | 692 | 0.11 | 0.46 | 0.98 | 0.34 | 0.06 | 0.55 | 0.85 |
| 1 | 1 | 695 | 4 | 863 | 0.04 | 0.55 | 0.92 | 0.81 | 1.63 | 0.55 | -0.54 |
| 1 | 1 | 696 | 8 | 344 | 0.06 | -0.17 | 0.92 | -1.18 | -1.11 | -0.61 | 0.11 |
| 1 | 1 | 697 | 6 | 238 | 0.04 | -0.48 | 0.09 | -1.18 | -1.11 | -0.61 | 0.58 |
| 1 | 1 | 698 | 3 | 312 | 0.07 | 0.28 | -0.78 | -0.58 | -0.72 | 0.55 | -2.31 |
| 1 | 1 | 699 | 3 | 579 | 0.06 | 0.49 | 0.09 | -0.11 | 0.06 | -0.61 | -1.08 |
| 1 | 1 | 700 | 6 | 401 | 0.07 | -0.92 | 0.92 | -0.08 | -0.72 | -0.61 | 0.27 |
| 1 | 1 | 701 | 3 | 860 | 0.06 | 0.20 | 0.92 | 0.06 | 0.06 | 2.88 | 1.52 |
| 1 | 1 | 702 | 7 | 653 | 0.05 | 0.64 | 0.92 | 0.33 | 0.06 | -0.61 | 0.10 |
| 1 | 1 | 703 | 4 | 624 | 0.04 | -0.67 | 0.92 | 0.33 | 0.06 | 0.55 | 0.44 |
| 1 | 1 | 704 | 4 | 261 | 0.1 | -0.64 | 0.09 | -1.18 | -1.11 | 0.55 | -0.02 |
| 1 | 1 | 705 | 5 | 544 | 0.09 | 0.56 | 0.09 | -0.59 | -0.72 | 0.55 | -2.34 |
| 1 | 1 | 706 | 7 | 775 | 0.19 | 2.21 | 0.92 | 0.69 | -0.16 | -0.61 | 0.09 |
| 1 | 1 | 707 | 2 | 469 | 0.02 | 0.84 | -0.78 | -0.53 | 0.06 | -0.61 | -2.39 |
| 1 | 1 | 708 | 4 | 754 | 0.02 | 0.58 | -1.20 | 0.85 | 1.63 | -0.61 | 0.82 |
| 1 | 1 | 709 | 4 | 136 | 0.02 | -1.09 | -1.20 | -1.12 | -0.72 | -0.61 | 0.24 |
| 1 | 1 | 710 | 5 | 52 | 0.05 | -1.70 | -1.20 | -1.17 | -0.72 | -0.61 | -0.48 |
| 1 | 1 | 711 | 8 | 294 | 0.11 | -0.16 | 0.09 | -0.77 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 712 | 8 | 255 | 0.06 | -0.88 | -0.78 | -0.66 | -0.72 | 0.55 | 0.79 |
| 1 | 1 | 713 | 7 | 208 | 0.03 | -0.76 | -0.78 | -1.11 | -0.72 | -0.61 | 0.05 |
| 1 | 1 | 714 | 4 | 444 | 0.07 | -0.34 | 0.92 | -0.08 | -0.72 | -0.61 | 0.69 |
| 1 | 1 | 715 | 2 | 77 | 0.03 | -1.68 | -1.20 | -1.17 | -0.72 | -0.61 | 0.16 |
| 1 | 1 | 716 | 6 | 542 | 0.03 | 0.37 | -0.78 | 0.33 | 0.06 | -0.61 | 0.14 |
| 1 | 1 | 717 | 2 | 1 | 0.03 | 0.84 | -0.78 | -0.60 | -0.72 | 2.88 | -2.38 |
| 1 | 1 | 718 | 6 | 33 | 0.08 | -1.48 | -1.20 | -1.18 | -1.11 | 0.55 | 0.69 |
| 1 | 1 | 719 | 3 | 26 | 0.06 | -0.02 | -1.20 | -0.69 | -0.72 | 2.88 | 0.72 |
| 1 | 1 | 720 | 8 | 370 | 0.06 | -0.11 | 0.09 | -0.68 | -0.72 | -0.61 | 0.10 |
| 1 | 1 | 721 | 4 | 848 | 0.03 | 1.09 | 0.09 | -0.08 | 1.63 | -0.61 | -2.25 |
| 1 | 1 | 722 | 6 | 617 | 0.06 | -0.07 | 0.09 | 0.34 | 0.06 | 0.55 | 0.97 |
| 1 | 1 | 723 | 3 | 53 | 0.07 | -1.14 | -0.92 | -1.18 | -1.11 | 0.55 | -0.44 |
| 1 | 1 | 724 | 10 | 507 | 0.1 | -0.58 | -0.82 | 0.21 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 725 | 10 | 248 | 0.05 | -0.77 | -0.78 | -0.63 | -0.72 | -0.61 | 0.80 |
| 1 | 1 | 726 | 2 | 55 | 0.04 | -1.81 | -1.20 | -0.68 | -0.72 | -0.61 | 1.27 |
| 1 | 1 | 727 | 10 | 104 | 0.06 | -0.14 | -0.78 | -0.63 | -0.72 | -0.61 | -2.33 |
| 1 | 1 | 728 | 4 | 888 | 0.07 | 1.71 | 0.92 | 0.42 | 1.63 | 0.55 | 0.22 |
| 1 | 1 | 729 | 12 | 216 | 0.03 | -1.13 | -0.78 | -0.67 | -0.72 | -0.61 | 0.45 |
| 1 | 1 | 730 | 1 | 567 | 0 | 0.71 | -0.78 | 0.33 | 0.06 | -0.61 | 0.24 |
| 1 | 1 | 731 | 4 | 996 | 0.16 | 3.40 | 2.67 | 1.77 | 0.06 | 2.88 | -0.54 |
| 1 | 1 | 732 | 7 | 660 | 0.03 | 0.72 | 0.92 | -0.53 | 0.06 | 0.55 | 0.45 |
| 1 | 1 | 733 | 3 | 337 | 0.07 | -0.95 | 0.09 | -0.63 | -0.72 | 0.55 | -0.63 |
| 1 | 1 | 734 | 4 | 801 | 0.12 | 1.46 | 0.09 | -0.38 | -0.72 | 2.88 | 0.44 |
| 1 | 1 | 735 | 4 | 13 | 0.03 | -0.76 | 0.92 | -1.29 | -1.11 | -0.61 | -2.25 |
| 1 | 1 | 736 | 4 | 57 | 0.11 | -0.81 | 1.15 | -1.18 | -1.11 | 0.55 | 1.27 |
| 1 | 1 | 737 | 3 | 324 | 0.06 | 0.49 | 0.09 | -0.82 | -0.72 | -0.61 | -2.35 |
| 1 | 1 | 738 | 3 | 577 | 0.02 | -0.51 | -0.78 | 0.82 | 0.06 | 0.55 | -0.62 |
| 1 | 1 | 739 | 7 | 108 | 0.06 | -1.43 | -1.20 | -0.83 | -0.72 | -0.61 | 0.91 |
| 1 | 1 | 740 | 7 | 747 | 0.08 | 1.56 | 0.92 | 0.83 | 0.06 | -0.61 | 0.74 |
| 1 | 1 | 741 | 8 | 262 | 0.05 | -1.27 | -0.78 | -0.08 | -0.72 | -0.61 | 0.42 |
| 1 | 1 | 742 | 6 | 90 | 0.05 | -1.56 | -0.78 | -0.69 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 743 | 8 | 273 | 0.04 | -0.62 | -0.78 | -0.62 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 744 | 5 | 845 | 0.16 | 2.53 | -0.78 | 0.46 | 0.06 | -0.61 | -2.18 |
| 1 | 1 | 745 | 9 | 225 | 0.04 | -0.94 | -0.78 | -0.65 | -0.72 | -0.61 | 0.81 |
| 1 | 1 | 746 | 3 | 287 | 0.05 | -0.98 | -0.78 | -0.08 | -0.72 | -0.61 | -0.18 |
| 1 | 1 | 747 | 4 | 284 | 0.04 | -0.92 | -0.78 | -0.08 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 748 | 6 | 186 | 0.04 | -0.80 | -0.78 | -1.08 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 749 | 3 | 962 | 0.09 | 1.73 | 0.92 | 3.06 | 1.63 | 0.55 | 1.00 |
| 1 | 1 | 750 | 3 | 922 | 0.13 | 2.37 | 0.92 | -0.30 | -0.72 | 2.88 | 0.08 |
| 1 | 1 | 751 | 6 | 926 | 0.09 | 0.53 | 0.92 | 1.80 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 752 | 3 | 93 | 0.02 | -1.16 | -0.78 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 753 | 6 | 749 | 0.05 | 0.61 | -1.20 | 0.82 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 754 | 5 | 463 | 0.05 | -0.17 | 0.92 | -0.08 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 755 | 5 | 265 | 0.05 | -0.75 | 0.92 | -1.18 | -1.11 | -0.61 | 0.45 |
| 1 | 1 | 756 | 8 | 400 | 0.06 | 0.27 | 0.09 | -0.66 | -0.72 | -0.61 | 0.11 |
| 1 | 1 | 757 | 4 | 583 | 0.15 | -0.27 | 2.67 | -0.53 | -0.72 | 0.55 | 1.27 |
| 1 | 1 | 758 | 3 | 325 | 0.07 | -1.27 | 0.92 | -0.54 | -0.72 | -0.61 | 0.85 |
| 1 | 1 | 759 | 3 | 893 | 0.02 | 1.02 | 1.38 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 760 | 6 | 297 | 0.04 | -0.90 | -0.78 | -0.08 | -0.72 | -0.61 | 0.45 |
| 1 | 1 | 761 | 2 | 982 | 0.05 | 4.59 | 0.92 | 0.80 | 0.06 | 2.88 | 0.33 |
| 1 | 1 | 762 | 13 | 260 | 0.08 | -0.45 | 0.09 | -1.18 | -1.11 | -0.61 | 0.10 |
| 1 | 1 | 763 | 3 | 796 | 0.05 | 0.60 | 2.67 | 0.86 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 764 | 2 | 290 | 0.03 | 0.19 | -0.78 | -1.15 | -0.72 | -0.61 | 0.75 |
| 1 | 1 | 765 | 12 | 953 | 0.18 | 2.20 | 0.92 | 0.14 | 1.63 | 2.88 | 0.50 |
| 1 | 1 | 766 | 4 | 696 | 0.12 | -0.50 | 0.92 | -0.13 | -0.72 | 2.88 | -0.73 |
| 1 | 1 | 767 | 3 | 160 | 0.07 | -0.30 | -0.92 | -0.68 | -0.72 | 0.55 | -1.67 |
| 1 | 1 | 768 | 2 | 548 | 0.05 | -0.22 | 2.67 | -0.68 | -0.72 | 0.55 | 0.27 |
| 1 | 1 | 769 | 4 | 45 | 0.03 | -1.52 | -1.20 | -1.18 | -1.11 | -0.61 | 0.97 |
| 1 | 1 | 770 | 5 | 524 | 0.1 | 0.18 | 0.92 | -0.65 | -0.72 | 0.55 | 1.01 |
| 1 | 1 | 771 | 2 | 152 | 0.01 | -1.72 | -0.78 | -0.70 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 772 | 1 | 905 | 0 | 0.54 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 773 | 3 | 417 | 0.06 | 0.05 | -0.78 | -0.58 | 0.06 | -0.61 | 0.79 |
| 1 | 1 | 774 | 6 | 616 | 0.03 | -0.01 | 0.09 | 0.34 | 0.06 | 0.55 | 0.37 |
| 1 | 1 | 775 | 4 | 665 | 0.14 | -0.34 | 0.92 | -0.29 | -0.72 | 2.88 | 0.08 |
| 1 | 1 | 776 | 4 | 473 | 0.07 | 0.45 | -0.78 | -0.08 | -0.72 | 0.55 | 0.70 |
| 1 | 1 | 777 | 2 | 464 | 0.04 | -1.30 | 0.09 | 0.90 | -0.72 | 0.55 | -1.07 |
| 1 | 1 | 778 | 4 | 37 | 0.09 | -2.06 | -0.78 | -1.21 | -1.11 | -0.61 | -0.19 |
| 1 | 1 | 779 | 6 | 875 | 0.07 | 0.69 | -0.78 | -0.54 | 0.06 | 2.88 | -2.28 |
| 1 | 1 | 780 | 9 | 363 | 0.05 | -0.21 | 0.09 | -0.68 | -0.72 | -0.61 | 0.51 |
| 1 | 1 | 781 | 7 | 158 | 0.02 | -0.73 | -0.78 | -1.18 | -1.11 | -0.61 | 0.06 |
| 1 | 1 | 782 | 3 | 562 | 0.02 | -0.45 | -0.78 | 0.82 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 783 | 5 | 382 | 0.1 | 0.07 | -0.78 | -0.08 | -0.72 | -0.61 | 1.19 |
| 1 | 1 | 784 | 10 | 976 | 0.09 | 1.28 | -0.78 | 3.06 | 3.19 | 0.55 | -0.58 |
| 1 | 1 | 785 | 2 | 927 | 0.01 | 1.22 | 0.92 | 1.73 | 1.63 | 0.55 | -0.62 |
| 1 | 1 | 786 | 3 | 578 | 0.06 | 0.27 | -0.78 | 0.87 | 0.06 | -0.61 | 0.75 |
| 1 | 1 | 787 | 5 | 201 | 0.04 | -1.29 | -0.78 | -0.65 | -0.72 | -0.61 | 0.30 |
| 1 | 1 | 788 | 5 | 393 | 0.04 | -0.20 | 1.38 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 789 | 3 | 188 | 0 | -0.28 | -0.78 | -1.12 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 790 | 5 | 945 | 0.02 | 1.35 | 2.67 | 0.81 | 1.63 | 0.55 | 1.52 |
| 1 | 1 | 791 | 6 | 632 | 0.02 | -0.44 | 0.92 | 0.32 | 0.06 | 0.55 | 0.07 |
| 1 | 1 | 792 | 3 | 612 | 0.07 | 0.13 | 0.92 | -0.56 | 0.06 | 0.55 | 0.18 |
| 1 | 1 | 793 | 3 | 234 | 0.04 | -0.39 | -1.20 | -0.82 | -0.72 | -0.61 | 0.72 |
| 1 | 1 | 794 | 1 | 487 | 0 | 0.20 | -0.78 | -0.08 | 0.06 | -0.61 | 0.63 |
| 1 | 1 | 795 | 4 | 523 | 0.02 | 0.12 | -0.78 | 0.33 | 0.06 | -0.61 | 0.28 |
| 1 | 1 | 796 | 7 | 281 | 0.02 | -0.43 | -0.78 | -0.68 | -0.72 | -0.61 | 0.63 |
| 1 | 1 | 797 | 14 | 808 | 0.15 | 0.43 | 2.67 | 0.47 | 0.06 | 0.55 | 0.51 |
| 1 | 1 | 798 | 7 | 296 | 0.04 | -0.41 | 0.92 | -1.18 | -1.11 | -0.61 | 0.81 |
| 1 | 1 | 799 | 3 | 307 | 0.01 | -0.62 | 0.09 | -0.89 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 800 | 4 | 825 | 0.06 | 1.22 | 0.09 | 0.82 | 1.63 | -0.61 | -0.44 |
| 1 | 1 | 801 | 4 | 620 | 0.12 | -0.17 | 0.09 | 0.22 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 802 | 4 | 726 | 0.01 | 0.29 | -0.78 | 0.82 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 803 | 7 | 556 | 0.02 | -0.54 | 0.92 | 0.31 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 804 | 4 | 514 | 0.04 | 0.08 | -1.20 | 0.33 | 0.06 | -0.61 | 0.37 |
| 1 | 1 | 805 | 4 | 885 | 0.1 | 1.63 | 0.09 | -0.08 | 0.06 | 2.88 | 0.92 |
| 1 | 1 | 806 | 3 | 824 | 0.02 | 1.27 | 0.09 | 0.86 | 1.63 | -0.61 | 0.83 |
| 1 | 1 | 807 | 8 | 280 | 0.04 | -0.45 | -0.78 | -0.70 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 808 | 9 | 871 | 0.11 | 0.77 | 0.97 | 0.82 | 1.63 | 0.55 | 1.01 |
| 1 | 1 | 809 | 7 | 872 | 0.15 | 1.43 | 0.92 | 0.71 | 1.63 | -0.61 | -0.62 |
| 1 | 1 | 810 | 4 | 278 | 0.03 | -1.15 | 0.92 | -1.08 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 811 | 2 | 966 | 0.01 | 1.47 | -0.78 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 812 | 9 | 249 | 0.06 | -1.37 | -0.78 | -0.08 | -0.72 | -0.61 | -0.09 |
| 1 | 1 | 813 | 9 | 942 | 0.27 | 0.91 | 2.67 | 0.45 | 0.06 | 2.88 | 0.77 |
| 1 | 1 | 814 | 7 | 328 | 0.05 | -0.03 | -0.78 | -0.60 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 815 | 2 | 14 | 0.03 | -0.75 | -0.78 | -0.69 | -0.72 | 2.88 | 1.01 |
| 1 | 1 | 816 | 3 | 483 | 0.05 | -0.02 | 0.92 | -0.08 | -0.72 | -0.61 | -0.40 |
| 1 | 1 | 817 | 11 | 264 | 0.04 | -0.58 | -0.78 | -0.66 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 818 | 2 | 853 | 0.01 | 0.75 | 0.92 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 819 | 3 | 762 | 0.02 | 0.77 | -1.20 | 0.82 | 1.63 | -0.61 | 0.58 |
| 1 | 1 | 820 | 6 | 611 | 0.09 | -0.76 | 0.92 | 0.13 | 0.06 | 0.55 | -0.40 |
| 1 | 1 | 821 | 6 | 23 | 0.03 | -0.89 | -1.20 | -0.96 | -0.72 | -0.61 | -2.36 |
| 1 | 1 | 822 | 4 | 376 | 0.09 | -0.57 | 0.09 | -0.65 | -0.72 | 0.55 | -0.19 |
| 1 | 1 | 823 | 5 | 804 | 0.04 | 0.56 | 0.92 | 0.82 | 1.63 | -0.61 | 0.47 |
| 1 | 1 | 824 | 4 | 47 | 0.04 | -1.27 | -0.78 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 825 | 4 | 932 | 0.13 | 2.77 | 0.92 | 0.48 | 0.06 | 0.55 | -2.36 |
| 1 | 1 | 826 | 5 | 534 | 0.02 | 0.24 | -0.78 | 0.33 | 0.06 | -0.61 | 0.81 |
| 1 | 1 | 827 | 8 | 895 | 0.08 | 0.55 | 0.09 | 1.73 | 1.63 | 0.55 | -0.70 |
| 1 | 1 | 828 | 5 | 940 | 0.04 | 0.98 | 2.67 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 829 | 5 | 361 | 0.05 | -0.32 | -0.78 | -0.54 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 830 | 5 | 783 | 0.03 | 1.23 | -0.78 | 0.82 | 1.63 | -0.61 | 0.16 |
| 1 | 1 | 831 | 4 | 715 | 0.07 | 0.86 | 0.92 | 0.44 | 0.06 | 0.55 | 0.18 |
| 1 | 1 | 832 | 4 | 383 | 0.04 | -0.42 | 0.92 | -0.64 | -0.72 | -0.61 | 0.82 |
| 1 | 1 | 833 | 10 | 403 | 0.24 | -0.27 | 2.67 | -0.56 | -0.72 | -0.61 | 0.42 |
| 1 | 1 | 834 | 5 | 335 | 0.07 | -0.30 | 0.92 | -1.17 | -1.03 | -0.61 | -0.44 |
| 1 | 1 | 835 | 4 | 331 | 0.03 | -0.89 | -0.78 | 0.33 | -0.72 | -0.61 | -0.33 |
| 1 | 1 | 836 | 11 | 683 | 0.12 | 0.44 | 1.38 | 0.42 | 0.06 | -0.61 | 1.29 |
| 1 | 1 | 837 | 4 | 27 | 0.05 | -0.95 | -0.78 | -0.08 | -0.72 | 2.88 | -0.46 |
| 1 | 1 | 838 | 7 | 587 | 0.08 | 0.20 | 0.09 | -0.56 | 0.06 | 0.55 | -1.08 |
| 1 | 1 | 839 | 7 | 352 | 0.06 | -0.66 | -0.78 | -0.61 | 0.06 | -0.61 | 0.09 |
| 1 | 1 | 840 | 8 | 619 | 0.07 | -0.39 | 0.92 | 0.83 | 0.06 | -0.61 | -0.02 |
| 1 | 1 | 841 | 8 | 333 | 0.07 | -0.29 | -0.78 | -0.64 | -0.72 | 0.55 | 0.82 |
| 1 | 1 | 842 | 11 | 185 | 0.04 | -1.36 | -0.78 | -0.73 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 843 | 3 | 414 | 0.04 | -0.76 | 0.09 | -0.08 | -0.72 | 0.55 | -0.50 |
| 1 | 1 | 844 | 7 | 826 | 0.06 | 0.96 | 0.92 | -0.08 | 1.63 | 0.55 | 0.54 |
| 1 | 1 | 845 | 3 | 955 | 0.01 | 0.97 | -0.78 | 3.06 | 3.19 | -0.61 | 0.07 |
| 1 | 1 | 846 | 8 | 190 | 0.03 | -1.15 | -0.78 | -0.67 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 847 | 7 | 661 | 0.19 | 0.88 | 0.92 | -0.22 | 0.06 | -0.61 | -1.25 |
| 1 | 1 | 848 | 4 | 301 | 0.12 | -1.11 | -0.78 | 0.02 | -0.72 | 0.55 | 1.08 |
| 1 | 1 | 849 | 3 | 472 | 0.03 | -0.35 | -0.78 | 0.33 | 0.06 | -0.61 | 0.18 |
| 1 | 1 | 850 | 2 | 327 | 0.04 | -0.81 | 0.09 | -0.68 | -0.72 | 0.55 | 1.27 |
| 1 | 1 | 851 | 11 | 164 | 0.05 | -1.07 | -1.20 | -0.70 | -0.72 | -0.61 | 0.90 |
| 1 | 1 | 852 | 5 | 943 | 0.04 | 1.48 | 2.67 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 853 | 2 | 701 | 0.01 | -0.08 | -0.78 | 0.82 | 1.63 | -0.61 | -0.30 |
| 1 | 1 | 854 | 14 | 282 | 0.2 | -1.35 | -0.78 | 0.16 | -0.72 | 0.55 | -0.75 |
| 1 | 1 | 855 | 3 | 923 | 0.01 | 0.97 | 0.92 | 1.73 | 1.63 | 0.55 | -0.62 |
| 1 | 1 | 856 | 2 | 78 | 0.02 | -0.93 | -1.20 | -1.18 | -1.11 | -0.61 | -0.44 |
| 1 | 1 | 857 | 6 | 380 | 0.08 | -0.21 | 1.23 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 858 | 2 | 118 | 0.03 | -1.23 | -1.20 | -0.68 | -0.72 | 0.55 | 0.89 |
| 1 | 1 | 859 | 9 | 650 | 0.11 | 0.95 | 0.92 | -0.11 | 0.06 | -0.61 | 0.10 |
| 1 | 1 | 860 | 5 | 452 | 0.03 | -0.75 | 0.92 | 0.32 | -0.72 | -0.61 | -0.33 |
| 1 | 1 | 861 | 9 | 870 | 0.04 | 0.13 | -0.78 | 1.73 | 1.63 | 0.55 | -0.77 |
| 1 | 1 | 862 | 5 | 16 | 0.08 | -0.84 | 0.09 | -1.29 | -1.11 | -0.61 | -2.28 |
| 1 | 1 | 863 | 9 | 858 | 0.08 | 1.16 | 0.92 | 0.36 | 1.63 | 0.55 | 0.24 |
| 1 | 1 | 864 | 5 | 257 | 0.07 | -0.87 | 0.09 | -0.68 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 865 | 8 | 564 | 0.07 | 0.10 | 0.09 | 0.33 | 0.06 | -0.61 | 0.33 |
| 1 | 1 | 866 | 5 | 630 | 0.07 | -0.50 | 0.92 | 0.24 | 0.06 | 0.55 | 0.96 |
| 1 | 1 | 867 | 6 | 856 | 0.04 | 1.27 | 0.92 | 0.84 | 1.63 | -0.61 | 0.78 |
| 1 | 1 | 868 | 3 | 415 | 0.1 | -0.10 | 0.09 | -0.62 | -0.72 | 0.55 | -1.28 |
| 1 | 1 | 869 | 5 | 126 | 0.02 | -1.12 | -1.20 | -1.12 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 870 | 3 | 61 | 0.09 | -1.82 | -0.78 | -0.50 | -1.11 | 0.55 | -0.16 |
| 1 | 1 | 871 | 5 | 588 | 0.05 | -0.41 | 0.09 | 0.82 | 0.06 | -0.61 | -1.30 |
| 1 | 1 | 872 | 5 | 326 | 0.05 | -1.02 | 0.92 | -0.68 | -0.72 | -0.61 | -0.75 |
| 1 | 1 | 873 | 2 | 924 | 0.06 | 2.44 | 0.92 | 0.57 | 1.63 | 0.55 | 0.04 |
| 1 | 1 | 874 | 1 | 411 | 0 | -1.81 | -0.78 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 875 | 4 | 88 | 0.02 | -1.50 | -0.78 | -1.18 | -1.11 | -0.61 | 0.37 |
| 1 | 1 | 876 | 5 | 286 | 0.03 | -0.39 | -0.78 | -0.68 | -0.72 | -0.61 | 0.45 |
| 1 | 1 | 877 | 4 | 593 | 0.07 | 0.34 | 0.92 | -0.55 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 878 | 2 | 340 | 0.02 | -0.63 | 0.92 | -1.08 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 879 | 4 | 635 | 0.05 | 0.30 | 0.09 | 0.32 | 0.06 | 0.55 | 0.26 |
| 1 | 1 | 880 | 6 | 379 | 0.05 | -0.46 | 0.92 | -0.72 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 881 | 2 | 105 | 0.01 | -0.98 | -0.78 | -1.12 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 882 | 5 | 490 | 0.1 | -0.34 | -0.87 | -0.08 | 0.06 | 0.55 | 0.14 |
| 1 | 1 | 883 | 4 | 877 | 0.04 | 1.09 | 1.38 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 884 | 3 | 106 | 0.02 | -1.17 | 0.09 | -1.18 | -1.11 | -0.61 | 1.01 |
| 1 | 1 | 885 | 4 | 673 | 0.05 | 0.61 | 0.92 | -0.08 | 0.06 | 0.55 | 0.53 |
| 1 | 1 | 886 | 3 | 554 | 0.02 | -0.16 | 0.09 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 887 | 3 | 416 | 0.07 | -1.19 | 0.09 | -0.08 | 0.06 | -0.61 | -0.92 |
| 1 | 1 | 888 | 4 | 664 | 0.06 | 0.71 | 0.92 | 0.33 | 0.06 | -0.61 | -0.44 |
| 1 | 1 | 889 | 4 | 18 | 0.04 | -1.60 | -1.20 | -1.18 | -1.11 | -0.61 | 1.52 |
| 1 | 1 | 890 | 3 | 553 | 0.03 | 0.38 | 0.09 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 891 | 5 | 449 | 0.04 | -0.06 | -1.20 | -0.08 | 0.06 | -0.61 | 0.08 |
| 1 | 1 | 892 | 8 | 647 | 0.06 | -0.37 | 0.92 | 0.32 | 0.06 | 0.55 | -0.44 |
| 1 | 1 | 893 | 10 | 453 | 0.04 | -0.57 | -0.78 | 0.33 | 0.06 | -0.61 | 0.45 |
| 1 | 1 | 894 | 5 | 443 | 0.04 | 0.25 | -0.78 | -0.53 | 0.06 | -0.61 | 0.17 |
| 1 | 1 | 895 | 5 | 604 | 0.08 | -0.07 | 0.09 | 0.82 | 0.06 | -0.61 | -0.63 |
| 1 | 1 | 896 | 6 | 970 | 0.24 | 2.95 | 0.92 | 0.64 | 1.63 | 2.88 | 0.52 |
| 1 | 1 | 897 | 8 | 157 | 0.11 | -0.85 | -0.83 | -0.71 | -0.72 | 0.55 | -1.16 |
| 1 | 1 | 898 | 6 | 314 | 0.04 | -0.12 | -0.78 | -0.68 | -0.72 | -0.61 | 0.56 |
| 1 | 1 | 899 | 7 | 268 | 0.02 | -0.59 | -0.78 | -0.68 | -0.72 | -0.61 | 0.42 |
| 1 | 1 | 900 | 5 | 74 | 0.04 | -1.31 | -1.20 | -1.18 | -1.11 | -0.61 | 0.09 |
| 1 | 1 | 901 | 3 | 573 | 0.05 | 0.87 | 0.92 | -0.08 | -0.72 | -0.61 | 0.73 |
| 1 | 1 | 902 | 4 | 398 | 0.03 | -0.76 | 0.09 | 0.31 | -0.72 | -0.61 | 0.40 |
| 1 | 1 | 903 | 2 | 894 | 0 | 0.29 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |
| 1 | 1 | 904 | 5 | 448 | 0.09 | -0.21 | -0.78 | -0.58 | 0.06 | 0.55 | -0.99 |
| 1 | 1 | 905 | 4 | 144 | 0.02 | -0.68 | -0.78 | -1.18 | -1.11 | -0.61 | 0.87 |
| 1 | 1 | 906 | 2 | 232 | 0 | -0.73 | -0.78 | -0.89 | -0.72 | -0.61 | 0.08 |
| 1 | 1 | 907 | 1 | 739 | 0 | 0.92 | -0.78 | -0.41 | -0.72 | 2.88 | -1.30 |
| 1 | 1 | 908 | 12 | 181 | 0.06 | -1.05 | -1.20 | -0.72 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 909 | 3 | 529 | 0.08 | -0.69 | 0.92 | -0.08 | 0.06 | -0.61 | 1.35 |
| 1 | 1 | 910 | 3 | 32 | 0.02 | -1.65 | -1.20 | -1.15 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 911 | 5 | 336 | 0.08 | -0.61 | 0.92 | -1.18 | -1.11 | 0.55 | 0.63 |
| 1 | 1 | 912 | 4 | 154 | 0.04 | -1.39 | -1.20 | -0.61 | -0.72 | -0.61 | 0.36 |
| 1 | 1 | 913 | 4 | 528 | 0.05 | 0.22 | 0.09 | -0.42 | 0.06 | -0.61 | -1.67 |
| 1 | 1 | 914 | 4 | 323 | 0.04 | -0.80 | 0.92 | -0.68 | -0.72 | -0.61 | -1.30 |
| 1 | 1 | 915 | 2 | 596 | 0.01 | 0.17 | 0.09 | 0.33 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 916 | 2 | 176 | 0.05 | -1.10 | -0.99 | -1.18 | 0.06 | -0.61 | 0.87 |
| 1 | 1 | 917 | 6 | 633 | 0.03 | 0.38 | 0.92 | 0.33 | 0.06 | -0.61 | 0.37 |
| 1 | 1 | 918 | 5 | 366 | 0.1 | -0.43 | -0.78 | -0.62 | 0.06 | -0.61 | -0.99 |
| 1 | 1 | 919 | 6 | 884 | 0.07 | 1.17 | 0.92 | 0.76 | 1.63 | 0.55 | -0.37 |
| 1 | 1 | 920 | 5 | 994 | 0.39 | 1.72 | 0.92 | 3.29 | 2.25 | 2.88 | -0.38 |
| 1 | 1 | 921 | 5 | 865 | 0.08 | 1.48 | 0.92 | -0.07 | 1.63 | 0.55 | 0.73 |
| 1 | 1 | 922 | 3 | 849 | 0.01 | 1.60 | 0.92 | -0.08 | 0.06 | 0.55 | -2.35 |
| 1 | 1 | 923 | 7 | 710 | 0.09 | 1.29 | -0.78 | -0.41 | -0.72 | 2.88 | -0.19 |
| 1 | 1 | 924 | 6 | 390 | 0.1 | 0.18 | 0.09 | -0.66 | -0.72 | -0.61 | 0.73 |
| 1 | 1 | 925 | 6 | 965 | 0.2 | 1.06 | 0.50 | 1.73 | 1.63 | 2.88 | -0.92 |
| 1 | 1 | 926 | 2 | 936 | 0.01 | 1.55 | 2.67 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 927 | 3 | 999 | 0.07 | 1.99 | 2.67 | 3.06 | 3.19 | 0.55 | 0.08 |
| 1 | 1 | 928 | 4 | 85 | 0.03 | -1.37 | -1.20 | -1.12 | -0.72 | -0.61 | -0.44 |
| 1 | 1 | 929 | 2 | 84 | 0.02 | -2.01 | -0.78 | -0.50 | -1.11 | -0.61 | 0.07 |
| 1 | 1 | 930 | 3 | 252 | 0.02 | 0.05 | -1.20 | -1.12 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 931 | 4 | 637 | 0.03 | -0.06 | 0.92 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 932 | 2 | 985 | 0.05 | 2.89 | 0.92 | 3.06 | 1.63 | -0.61 | -1.37 |
| 1 | 1 | 933 | 13 | 12 | 0.08 | -1.30 | -1.20 | -0.94 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 934 | 8 | 725 | 0.12 | 0.72 | 0.92 | -0.58 | 0.06 | 0.55 | -2.27 |
| 1 | 1 | 935 | 3 | 708 | 0.1 | 1.56 | 0.92 | 0.07 | -0.72 | 0.55 | -0.39 |
| 1 | 1 | 936 | 4 | 462 | 0.07 | -0.57 | 0.92 | 0.34 | -0.72 | -0.61 | 0.83 |
| 1 | 1 | 937 | 2 | 978 | 0.01 | 1.72 | 0.92 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 938 | 4 | 272 | 0.02 | -0.57 | -0.78 | -0.68 | -0.72 | -0.61 | 0.24 |
| 1 | 1 | 939 | 3 | 935 | 0.19 | 1.90 | 0.08 | 0.87 | 0.06 | 2.88 | -1.08 |
| 1 | 1 | 940 | 6 | 470 | 0.02 | -0.35 | -0.78 | 0.33 | 0.06 | -0.61 | 1.01 |
| 1 | 1 | 941 | 4 | 964 | 0.03 | 1.38 | -0.78 | 3.06 | 3.19 | -0.61 | 0.07 |
| 1 | 1 | 942 | 6 | 456 | 0.13 | -0.63 | 0.09 | -0.08 | -0.72 | 2.88 | -0.15 |
| 1 | 1 | 943 | 4 | 526 | 0.09 | -0.41 | -0.89 | 0.85 | 0.06 | -0.61 | 1.52 |
| 1 | 1 | 944 | 3 | 17 | 0.1 | -1.41 | -1.06 | -1.16 | -0.98 | 0.55 | 1.52 |
| 1 | 1 | 945 | 7 | 855 | 0.07 | 2.44 | 0.92 | 0.79 | 0.06 | 0.55 | 0.17 |
| 1 | 1 | 946 | 3 | 125 | 0.02 | -1.17 | 0.09 | -1.18 | -1.11 | -0.61 | 0.77 |
| 1 | 1 | 947 | 9 | 198 | 0.03 | -1.30 | -0.78 | -0.65 | -0.72 | -0.61 | 0.50 |
| 1 | 1 | 948 | 10 | 22 | 0.16 | -0.20 | 2.67 | -0.88 | -0.87 | -0.61 | 1.52 |
| 1 | 1 | 949 | 5 | 677 | 0.08 | 0.30 | 0.92 | 0.24 | 0.06 | 0.55 | -0.38 |
| 1 | 1 | 950 | 7 | 539 | 0.05 | -0.09 | -0.78 | 0.83 | 0.06 | -0.61 | 0.50 |
| 1 | 1 | 951 | 3 | 728 | 0.09 | 1.50 | 0.92 | -0.55 | 0.06 | -0.61 | -2.15 |
| 1 | 1 | 952 | 4 | 512 | 0.1 | 0.03 | 0.92 | -0.69 | -0.72 | 0.55 | -1.23 |
| 1 | 1 | 953 | 12 | 80 | 0.07 | -0.46 | 0.09 | -0.96 | -0.72 | -0.61 | -2.29 |
| 1 | 1 | 954 | 9 | 659 | 0.04 | 0.68 | 0.92 | 0.33 | 0.06 | -0.61 | 0.81 |
| 1 | 1 | 955 | 5 | 797 | 0.04 | 1.28 | 0.09 | 0.46 | 1.63 | -0.61 | 0.08 |
| 1 | 1 | 956 | 2 | 713 | 0.01 | 0.12 | -1.20 | 0.82 | 1.63 | -0.61 | 0.37 |
| 1 | 1 | 957 | 7 | 233 | 0.01 | -0.92 | -0.78 | -0.68 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 958 | 8 | 43 | 0.04 | -0.89 | -0.78 | -0.89 | -0.72 | -0.61 | -2.30 |
| 1 | 1 | 959 | 5 | 810 | 0.03 | 1.03 | 0.09 | 0.83 | 1.63 | -0.61 | 0.76 |
| 1 | 1 | 960 | 7 | 345 | 0.11 | -0.30 | -0.78 | -0.57 | -0.72 | 0.55 | 0.16 |
| 1 | 1 | 961 | 3 | 173 | 0.09 | -1.36 | 0.92 | -0.81 | -0.72 | -0.61 | -1.07 |
| 1 | 1 | 962 | 5 | 169 | 0.03 | -0.83 | 0.09 | -1.18 | -1.11 | -0.61 | 0.89 |
| 1 | 1 | 963 | 5 | 71 | 0.06 | -1.21 | -1.20 | -0.98 | -0.72 | -0.61 | -1.08 |
| 1 | 1 | 964 | 9 | 478 | 0.03 | -0.31 | -0.78 | 0.33 | 0.06 | -0.61 | 0.43 |
| 1 | 1 | 965 | 1 | 129 | 0 | 0.89 | -0.78 | -0.60 | -0.72 | -0.61 | -2.37 |
| 1 | 1 | 966 | 4 | 543 | 0.15 | 0.25 | -0.78 | -0.18 | 0.06 | 0.55 | 0.21 |
| 1 | 1 | 967 | 13 | 984 | 0.16 | 1.49 | 0.09 | 3.06 | 3.19 | 0.55 | -0.92 |
| 1 | 1 | 968 | 8 | 117 | 0.03 | -0.99 | -0.78 | -1.18 | -1.11 | -0.61 | 0.88 |
| 1 | 1 | 969 | 6 | 275 | 0.1 | -0.66 | -0.92 | -0.65 | -0.72 | 0.55 | -0.45 |
| 1 | 1 | 970 | 1 | 843 | 0 | 0.55 | 0.92 | 0.82 | 1.63 | -0.61 | 1.52 |
| 1 | 1 | 971 | 4 | 149 | 0.05 | -1.55 | -0.78 | -0.61 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 972 | 5 | 541 | 0.08 | 0.03 | 0.09 | -0.62 | 0.06 | 0.55 | 0.30 |
| 1 | 1 | 973 | 4 | 426 | 0.07 | -0.40 | -0.78 | -0.53 | 0.06 | 0.55 | 0.77 |
| 1 | 1 | 974 | 4 | 738 | 0.13 | 0.17 | 0.09 | -0.08 | 0.06 | 2.88 | -0.29 |
| 1 | 1 | 975 | 14 | 920 | 0.19 | 2.87 | 0.68 | 0.55 | 0.06 | -0.61 | -2.32 |
| 1 | 1 | 976 | 4 | 163 | 0.05 | -1.80 | -0.78 | -0.08 | -0.72 | -0.61 | -0.46 |
| 1 | 1 | 977 | 9 | 681 | 0.11 | 0.27 | -0.78 | -0.19 | 0.06 | 2.88 | 0.12 |
| 1 | 1 | 978 | 5 | 821 | 0.15 | 1.09 | 0.92 | -0.73 | -0.72 | 2.88 | 0.83 |
| 1 | 1 | 979 | 6 | 476 | 0.16 | 1.03 | 0.09 | -0.47 | -0.72 | -0.61 | 0.45 |
| 1 | 1 | 980 | 5 | 689 | 0.12 | 0.18 | 1.01 | 0.17 | 0.06 | 0.55 | 1.52 |
| 1 | 1 | 981 | 6 | 113 | 0.07 | -0.63 | -0.78 | -0.89 | -0.72 | -0.61 | -1.67 |
| 1 | 1 | 982 | 5 | 766 | 0.04 | 0.31 | 2.67 | 0.82 | 0.06 | -0.61 | 0.07 |
| 1 | 1 | 983 | 4 | 394 | 0.03 | -0.23 | 0.92 | -0.68 | -0.72 | -0.61 | 1.01 |
| 1 | 1 | 984 | 7 | 974 | 0.04 | 1.04 | -0.78 | 3.06 | 3.19 | 0.55 | -0.81 |
| 1 | 1 | 985 | 6 | 938 | 0.18 | 2.61 | 0.92 | 0.33 | 0.06 | 2.88 | 0.11 |
| 1 | 1 | 986 | 4 | 672 | 0.09 | 2.02 | -0.78 | 0.46 | 0.06 | -0.61 | 0.52 |
| 1 | 1 | 987 | 5 | 422 | 0.04 | -0.07 | 0.92 | -0.61 | -0.72 | -0.61 | 0.07 |
| 1 | 1 | 988 | 3 | 395 | 0.06 | -0.58 | 0.09 | 0.18 | -0.72 | -0.61 | 1.52 |
| 1 | 1 | 989 | 5 | 866 | 0.04 | 0.56 | 0.92 | 1.73 | 1.63 | -0.61 | 0.07 |
| 1 | 1 | 990 | 8 | 348 | 0.06 | -0.05 | -0.78 | -0.56 | 0.06 | -0.61 | -2.30 |
| 1 | 1 | 991 | 3 | 734 | 0.02 | 0.35 | -0.78 | 0.82 | 1.63 | -0.61 | 1.01 |
| 1 | 1 | 992 | 5 | 961 | 0.01 | 1.22 | -0.78 | 3.06 | 3.19 | -0.61 | 0.47 |
| 1 | 1 | 993 | 4 | 756 | 0.14 | 0.12 | -0.78 | 0.36 | 0.06 | 2.88 | 1.39 |
| 1 | 1 | 994 | 6 | 755 | 0.03 | 0.74 | -0.78 | 0.84 | 1.63 | -0.61 | 0.78 |
| 1 | 1 | 995 | 7 | 975 | 0.27 | 1.37 | 2.67 | 0.68 | 1.63 | 2.88 | 0.55 |
| 1 | 1 | 996 | 4 | 205 | 0.02 | -1.27 | 0.09 | -1.08 | -0.72 | -0.61 | 0.06 |
| 1 | 1 | 997 | 6 | 320 | 0.03 | -0.06 | -0.78 | -0.68 | -0.72 | -0.61 | 0.10 |
| 1 | 1 | 998 | 3 | 65 | 0.04 | -0.56 | -0.78 | -0.08 | -0.72 | 2.88 | 0.07 |
| 1 | 1 | 999 | 2 | 628 | 0.01 | -0.21 | 0.92 | 0.82 | 0.06 | -0.61 | 0.47 |
| 1 | 1 | 1000 | 5 | 218 | 0.05 | -1.22 | -0.78 | -0.63 | -0.72 | 0.55 | 0.51 |
| 1 | 1 | 1001 | 4 | 180 | 0.04 | -1.01 | -0.78 | -1.12 | -0.72 | -0.61 | 0.44 |
Now let’s check the compression summary. The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.
compressionSummaryTable(map_C[[3]]$compression_summary)| segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold | parameters |
|---|---|---|---|---|
| 1 | 1001 | 829 | 0.83 | n_cells: 1001 quant.err: 0.1 distance_metric: L1_Norm error_metric: max quant_method: kmeans |
It can be observed from the table above that 829 cells out of
1001 i.e. 83% of the cells has hit the
Quantization Error threshold
Now let’s plot the Voronoi Tessellation with the heatmap overlaid for all the features in the computers dataset for better visualization.
metric_list <- colnames(trainComputers)
hmap <- list()
hmap <- lapply(1:length(metric_list), function(x){
muHVT::hvtHmap(
map_C,
trainComputers,
child.level = 1,
hmap.cols = metric_list[[x]],
line.width = c(0.2),
color.vec = c("#141B41"),
palette.color = 6,
centroid.size = 0.01,
show.points = T,
quant.error.hmap = 0.1,
n_cells.hmap = 1001
)
})The heatmaps displayed below provides a visual representation of the spatial characteristics of the computers data, allowing us to observe patterns and trends in the distribution of each of the features (price,speed,hd,ram,screen,ads). The sheer green shades highlight regions with higher values in each of the heatmaps, while the indigo shades indicate areas with the lowest values in each of the heatmaps. By analyzing these heatmaps, we can gain insights into the variations and relationships between each of these features within the computers data
grid.arrange(hmap[[1]], nrow = 1, ncol=1)grid.arrange(hmap[[2]], nrow = 1, ncol=1)grid.arrange(hmap[[3]], nrow = 1, ncol=1)grid.arrange(hmap[[4]], nrow = 1, ncol=1)grid.arrange(hmap[[5]], nrow = 1, ncol=1)grid.arrange(hmap[[6]], nrow = 1, ncol=1)We now have the set of maps (map A, map B & map C) which will be used to predict which map and cell each test record is assigned to, but before that lets view our test dataset
Now, lets have a look at the scaled testing dataset containing (1252 data points). For the shake of brevity we are displaying first six rows.
#testComputers <- scale(testComputers, center = scale_attr$`scaled:center`, scale = scale_attr$`scaled:scale`)
testComputers1 <- testComputers %>% as.data.frame() %>% round(4)
Table(head(testComputers1))| price | speed | hd | ram | screen | ads | |
|---|---|---|---|---|---|---|
| 5008 | -1.2287 | -0.7832 | -0.6760 | -0.7181 | 0.5490 | -0.8403 |
| 5009 | 1.3848 | 0.0922 | 3.0631 | 3.1928 | 0.5490 | -0.8403 |
| 5010 | -0.8016 | 0.0922 | -0.6760 | -0.7181 | -0.6148 | -0.8403 |
| 5011 | 0.2311 | 2.6668 | -0.4096 | -0.7181 | -0.6148 | -0.8403 |
| 5012 | 0.3084 | 0.9161 | 1.7311 | 1.6285 | 0.5490 | -0.8403 |
| 5013 | -0.5072 | 0.9161 | 3.0631 | 0.0641 | -0.6148 | -0.8403 |
For deatiled information about the prediction please refer to section 5 of this vignette.
Now once we have built the model, let us try to predict using
predictLayerHVT function and our test dataset which cell
and which layer each point belongs to.
predictLayerHVT(data,
map_A,
map_B,
map_C,
mad.threshold = 0.2,
normalize = T,
distance_metric="L1_Norm",
error_metric="max",
child.level = 1,
line.width = c(0.6, 0.4, 0.2),
color.vec = c("#141B41", "#6369D1", "#D8D2E1"),
yVar= NULL,
...)Each of the parameters of predictLayerHVT function has been explained below:
data - A dataframe containing the
test dataset. The dataframe should have atleast one variable used for
training. The variables from this dataset can also be used to overlay as
heatmap
map A - A list of hvt.results.model
obtained from HVT function while performing hierarchical vector
quantization on train data
map B - A list of hvt.results.model
obtained from HVT function while performing hierarchical vector
quantization on train data with novelty
map C - A list of hvt.results.model
obtained from HVT function while performing hierarchical vector
quantization on train data without novelty
child.level - A number indicating
the layer for which the heat map is to be plotted.(Only used if
hmap.cols is not NULL)
mad.threshold - A numeric values
indicating the permissible Mean Absolute Deviation
normalize - A logical value
indicating if the columns in your dataset should be normalized. Default
value is TRUE.
distance_metric - The distance
metric can be ’Euclidean” or “Manhattan”. Euclidean is selected by
default.
error_metric - The error metric can
be “mean” or “max”. mean is selected by default
yVar - Name of the dependent
variable(s)
... - color.vec and line.width can
be passed from here
Let’s see which cell and layer each point belongs to. For the shake of brevity we are displaying first twenty rows.
validation_data <- testComputers
new_predict <- predictLayerHVT(
data=validation_data,
map_A,
map_B,
map_C,
normalize = F
)
new_predict %>% head(100) %>%
as.data.frame() %>%
Table(scroll = T, limit = 20)| Row.Number | Layer1.Cell.ID | Layer2.Cell.ID |
|---|---|---|
| 1 | A155 | C170 |
| 2 | A987 | C984 |
| 3 | A312 | C318 |
| 4 | A663 | C652 |
| 5 | A913 | C914 |
| 6 | A848 | C847 |
| 7 | A987 | C984 |
| 8 | A521 | C510 |
| 9 | A433 | C424 |
| 10 | A145 | C145 |
| 11 | A976 | C974 |
| 12 | A503 | C533 |
| 13 | A903 | C895 |
| 14 | A955 | C949 |
| 15 | A621 | C641 |
| 16 | A903 | C895 |
| 17 | A312 | C189 |
| 18 | A621 | C557 |
| 19 | A224 | C318 |
| 20 | A989 | C986 |
Now, lets understand the output of
predictLayerHVT function.
Layer1.Cell.ID - Contains Cell.ids
from map A depending on the number of cells provided as input
Layer2.Cell.ID - Contains Cell.ids
from map B (novelty) and map C.
Example Usage1: Visualizing Multidimensional Data with Sammon’s Projection using Torus (Donut)
We have considered torus dataset for multidimensional data visualization using sammons projection.
We constructed a compressed HVT map (hvt.torus) by applying the
HVT() on the torus dataset. We set the parameters as follows:
n_cells = 100, quant.error = 0.1, and
depth = 1. Upon analyzing the compression summary, we found
that none of the 100 cells exceeded the quantization threshold
error.
We created another compressed HVT map (hvt.torus2) using the
HVT() algorithm on the torus dataset. This time, we adjusted the
parameters to n_cells = 300,
quant.error = 0.1, and depth = 1. After
examining the compression summary, we discovered that 14% of the cells
hit the quantization threshold error.
Once again, we generated a compressed HVT map (hvt.torus3) using
the HVT() algorithm on the torus dataset. The parameters for this map
were set to n_cells = 900, quant.error = 0.1,
and depth = 1. Upon analyzing the compression summary, we
found that 93% of the 100 cells hit the quantization threshold error and
we can clearly visualize the 3D torus(donut) in 2D space.
Example Usage2: Example Usage of Predictions using the predictLayerHVT on Personal Computers Dataset
We have considered computers dataset for creating a predictive set of maps to monitor entities over time using predictLayerHVT() in this notebook
We construct a compressed HVT map (Map A) using the HVT() on the
training dataset by setting n_cells to
1001 and quant.error to 0.1
Based on the output of the above step, we manually identify the novelty cell(s) from the plotted map A. For this dataset, we identify the 213th and 384th cells as the novelty cell.
We pass the identified novelty cell(s) as a parameter to the removeNovelty() along with HVT map A. The function removes that novelty cell(s) from the dataset and stores them separately. It also returns the dataset without novelty(s).
The plotCells() constructs hierarchical voronoi tessellations and highlights the identified novelty cell(s) in red
The dataset with novelty is then passed to the HVT() to construct
another HVT map (map B). But here, we set the parameters
n_cells = 3,
depth = 1 etc. when constructing the
map
The dataset without novelties is then passed to the HVT() to
construct another HVT map (map C). But here, we set the parameters
n_cells = 1001,
depth = 1 etc. when constructing the
map.
Finally, the set of maps - map A, map B, and map C are passed to the predictLayerHVT() along with the test dataset to predict which map and what cell each test record is assigned to.
The output of predictLayerHVT is a dataset with two columns Layer1.Cell.ID and Layer2.Cell.ID. Layer1.Cell.ID contains cell ids from map A in the form A1,A2,A3…. and Layer2.Cell.ID contains cell ids from map B as B1,B2… depending on the identified novelties and map C as C1,C2,C3…..
Pricing Segmentation - The package can be used to discover groups of similar customers based on the customer spend pattern and understand price sensitivity of customers
Market Segmentation - The package can be helpful in market segmentation where we have to identify micro and macro segments. The method used in this package can do both kinds of segmentation in one go
Anomaly Detection - This method can help us categorize system behavior over time and help us find anomaly when there are changes in the system. For e.g. Finding fraudulent claims in healthcare insurance
The package can help us understand the underlying structure of the data. Suppose we want to analyze a curved surface such as sphere or vase, we can approximate it by a lot of small low-order polygons in the form of tessellations using this package
In biology, Voronoi diagrams are used to model a number of different biological structures, including cells and bone microarchitecture
Using the base idea of Systems Dynamics, these diagrams can also be used to depict customer state changes over a period of time
Topology Preserving Maps : https://link.springer.com/chapter/10.1007/1-84628-118-0_7
Vector Quantization : https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-450-principles-of-digital-communications-i-fall-2006/lecture-notes/book_3.pdf
Sammon’s Projection : http://en.wikipedia.org/wiki/Sammon_mapping
Voronoi Tessellations : http://en.wikipedia.org/wiki/Centroidal_Voronoi_tessellation
Embedding : https://en.wikipedia.org/wiki/Embedding